Association of Estimated Pulse Wave Velocity and Metabolic Markers With Cognitive Impairment Risk in Diabetes

Association of Estimated Pulse Wave Velocity and Metabolic Markers With Cognitive Impairment Risk in Diabetes | 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 of Estimated Pulse Wave Velocity and Metabolic Markers With Cognitive Impairment Risk in Diabetes Lingjie Kong, Guangning Zhang, Jun Wu, Xizhen Zhou, Linfei Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7835974/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Mar, 2026 Read the published version in BMC Endocrine Disorders → Version 1 posted 10 You are reading this latest preprint version Abstract Background Type 2 diabetes mellitus (T2DM) is a major modifiable risk factor for dementia, yet cost-effective, scalable methods for early identification of cognitively at-risk individuals remain limited. Objective To evaluate the predictive value of estimated pulse wave velocity (ePWV), alone and in combination with metabolic markers, for incident cognitive impairment (CI) in adults with diabetes. Methods Using data from 893 participants with T2DM in the China Health and Retirement Longitudinal Study (CHARLS), we prospectively assessed the association of ePWV—calculated from routine blood pressure and age—with CI over a 9-year follow-up. Metabolic indicators, including the triglyceride–glucose (TyG) index and glycated hemoglobin (HbA1c), were integrated into Cox regression and restricted cubic spline analyses. Results Higher ePWV was independently associated with an increased risk of CI, even after adjusting for conventional vascular and metabolic covariates. Models combining ePWV with TyG index and HbA1c demonstrated superior predictive discrimination compared with ePWV alone. Conclusions ePWV, obtainable from routine clinical measurements, is a practical and scalable tool to identify diabetic individuals at elevated risk for CI. Combining vascular stiffness and metabolic metrics enhances risk stratification, highlighting dual targets for early preventive intervention. These findings support the integration of cardiovascular–metabolic monitoring into dementia prevention strategies for high-risk populations. Estimated pulse wave velocity Diabetes mellitus Cognitive impairment Arterial stiffness Metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Dementia is a progressive neurodegenerative syndrome, most often caused by Alzheimer’s disease (AD), which accounts for 60–70% of cases in adults ≥ 65 years.[ 1 ] By 2050, over 100 million people are projected to be affected, posing an immense global healthcare burden. Type 2 diabetes mellitus (T2DM) substantially increases the risk of cognitive impairment, vascular dementia (VaD), and AD.[ 2 ] Compared with non-diabetic individuals, those with T2DM have ~ 1.5 fold higher risk of AD, more than double the risk of VD, and greater likelihood of progressing from mild cognitive impairment (MCI) to dementia.[ 2 ] Parallels between cerebral insulin resistance in AD and systemic insulin dysregulation in T2DM underpin the “type 3 diabetes” hypothesis.[ 3 ] Hypertension, common in T2DM, accelerates cognitive decline, partly via arterial stiffness (AS)—a vascular aging marker linked to insulin resistance and adverse cerebrovascular outcomes.[ 4 , 5 ] The brain’s high flow circulation is particularly vulnerable to the hemodynamic effects of aortic stiffening.[ 6 ] Carotid–femoral pulse wave velocity (cfPWV) is the gold standard for AS assessment but requires specialized equipment, limiting its scalability.[ 7 ] Evidence on AS and dementia risk is inconsistent. Estimated PWV (ePWV), calculated from age and mean blood pressure, is a practical surrogate that predicts cardiovascular outcomes comparably to cfPWV.[ 8 , 9 ] Recent studies suggest that increased AS is associated with higher PWV measurements among individuals with prediabetes and diabetes.[ 10 , 11 ] Systemic inflammation is another pathway linking T2DM to cognitive decline. C reactive protein (CRP) correlates with worse cognition in T2DM and MCI.[ 12 , 13 ] Obesity amplifies vascular and metabolic stress, [ 14 ] while biomarkers such as the triglyceride–glucose (TyG) index offer low-cost risk profiling.[ 15 ] Yet, the joint influence of ePWV and metabolic–inflammatory markers (TyG, CRP, BMI, HbA1c) on cognition in T2DM remains poorly understood. We therefore examined associations between ePWV and cognitive performance in diabetic adults, evaluating effect modification by TyG, CRP, BMI, and HbA1c. 2. Methods 2.1 Study Design This study used data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort of adults aged ≥ 45, recruited in 2011 via multistage stratified sampling.[ 16 ] Five waves (2011–2020) provided sociodemographic and health data. Study protocols were ethics-approved by Peking University, with all participants providing informed consent. 2.2 Study population From 17,708 Wave 1 participants, exclusions included non-diabetics, age < 45/missing age, baseline cognitive impairment, and extreme ePWV values, yielding 893 diabetic participants for analysis ( Fig. 1 ) . Baseline characteristics of included versus excluded individuals are shown in Table 1 . Table 1 Baseline characters Characteristic Overall N = 17,705 Excluded N = 16,814 Included N = 891 P value SMD Age 58.0 [51.0, 65.0] 58.0 [51.0, 65.0] 59.0 [54.0, 64.0] 0.003 0.05 Gender 0.093 0.06 Female 9,222 (52%) 8,783 (52%) 439 (49%) Male 8,471 (48%) 8,020 (48%) 451 (51%) Time since diagnosis 3.0 [1.0, 7.0] 3.0 [1.0, 6.0] 4.0 [2.0, 9.0] < 0.001 0.21 Residence < 0.001 0.22 City/Town 1,912 (11%) 1,751 (10%) 161 (18%) Village 15,759 (89%) 15,030 (90%) 729 (82%) Education < 0.001 0.25 Primary or below 11,755 (66%) 11,264 (67%) 491 (55%) High school 5,521 (31%) 5,155 (31%) 366 (41%) Tertiary school 429 (2%) 395 (2%) 34 (4%) Smoking < 0.001 0.17 Non-smoker 11,426 (65%) 10,909 (65%) 517 (58%) Ex-smoker 1,417 (8%) 1,308 (8%) 109 (12%) Current smoker 4,862 (27%) 4,597 (27%) 265 (30%) Drinking 0.965 0.01 None of these 11,938 (67%) 11,338 (67%) 600 (67%) Drink but less than once a month 1,384 (8%) 1,316 (8%) 68 (8%) Drink more than once a month 4,383 (25%) 4,160 (25%) 223 (25%) Lipid lowering drug < 0.001 0.35 Not take 16,792 (95%) 16,035 (95%) 757 (85%) Take 913 (5%) 779 (5%) 134 (15%) Anti-hypertension drug < 0.001 0.37 Not take 13,300 (75%) 12,774 (76%) 526 (59%) Take 4,405 (25%) 4,040 (24%) 365 (41%) Stroke 0.031 0.07 No 17,111 (98%) 16,251 (98%) 860 (97%) Yes 413 (2%) 382 (2%) 31 (3%) Heart disease < 0.001 0.22 No 15,372 (88%) 14,657 (88%) 715 (81%) Yes 2,093 (12%) 1,920 (12%) 173 (19%) CSED 9.0 [6.0, 13.0] 9.0 [6.0, 13.0] 9.0 [6.0, 12.0] < 0.001 0.12 Sleeping time 6.0 [5.0, 8.0] 6.0 [5.0, 8.0] 7.0 [5.0, 8.0] 0.675 0.03 Waist circumference 84.4 [77.6, 92.0] 84.0 [77.2, 91.2] 90.0 [83.0, 97.0] < 0.001 0.44 BMI 23.1 [20.8, 25.7] 23.0 [20.7, 25.6] 24.8 [22.5, 27.6] < 0.001 0.45 HbA1C 5.1 [4.9, 5.4] 5.1 [4.9, 5.4] 5.9 [5.3, 7.3] < 0.001 1.00 TyG 8.6 [8.2, 9.0] 8.6 [8.2, 9.0] 9.5 [9.0, 10.0] < 0.001 1.29 FPG 102.2 [94.5, 112.7] 101.3 [94.0, 110.0] 150.8 [135.5, 191.0] < 0.001 1.42 eGFR 90.5 [76.5, 100.5] 90.4 [76.5, 100.5] 92.5 [76.1, 102.3] 0.064 0.08 Triglycerides (mmol/l) 1.2 [0.8, 1.8] 1.2 [0.8, 1.7] 1.7 [1.1, 2.8] < 0.001 0.56 Ldl Cholesterol (mmol/l) 2.9 [2.4, 3.5] 2.9 [2.4, 3.5] 3.0 [2.3, 3.6] 0.757 0.05 Total Cholesterol (mmol/l) 4.9 [4.3, 5.6] 4.9 [4.3, 5.5] 5.2 [4.5, 6.0] < 0.001 0.30 Hdl Cholesterol (mmol/l) 1.3 [1.0, 1.5] 1.3 [1.0, 1.5] 1.1 [0.9, 1.4] < 0.001 0.42 CRP (mg/l) 1.0 [0.6, 2.2] 1.0 [0.5, 2.1] 1.5 [0.7, 3.1] < 0.001 0.10 ePWV (m/s) 9.5 (1.9) 9.5 (2.0) 9.8 (1.6) < 0.001 0.15 ePWV*BMI 222.9 (57.3) 221.5 (57.0) 246.0 (56.3) < 0.001 0.43 ePWV*CRP 9.7 [5.0, 21.2] 9.4 [4.9, 20.5] 14.0 [7.0, 30.6] < 0.001 0.90 ePWV*TyG 82.8 (18.0) 82.0 (17.8) 92.8 (16.9) < 0.001 0.62 ePWV*HbA1C 50.3 (13.3) 49.4 (12.2) 63.1 (19.7) < 0.001 0.84 2.3 ePWV and Calculated variables We derived parameters using established formulas: Mean Arterial Pressure (MBP): MBP = Diastolic BP + 0.4 * Pulse Pressure, ePWV: ePWV = 9.587 - (0.402 * age) + (0.00456 * age²) - (0.00002621 * age² * MBP) + (0.003176 * age * MBP) - (0.01832 * MBP), TyG: TyG = ln[Triglycerides (mg/dL) * Fasting Glucose (mg/dL) / 2], Body Mass Index (BMI):BMI = Weight (kg) / [Height (cm)/100]², Interaction Terms: ePWV * BMI, ePWV * CRP (mg/l), ePWV * HbA1c(%), ePWV * TyG. 2.4 Assessment of cognition function Cognition was evaluated in 2013, 2015, 2018, and 2020 using the Chinese version of the Mini-Mental State Examination (MMSE), covering episodic memory and mental intactness. Episodic memory was tested via immediate and 5 minute delayed recall of 10 Chinese words (1 point per correct; range: 0–20). Mental intactness was assessed with the Telephone Interview of Cognitive Status (TICS) and a visuospatial task. TICS measured temporal orientation (year, month, day, season, weekday) and serial subtraction (7 from 100 up to five times). Visuospatial ability involved reproducing a presented figure (correct = 1). Scores for mental intactness ranged from 0 to 11. Global cognition was the sum of both domains (range: 0–31), higher scores indicating better function. Cognitive impairment (CI) was defined as global cognition < 11 or being in the lowest quartile for mental intactness (< 2.75) or episodic memory (< 5). [ 17 – 20 ] CI onset was the period between the last normal assessment and the first CI record. 2.5 Assessment of covariates Baseline CHARLS data included: (1) Demographics—age, sex, education, residence type, healthcare access; (2) Physical measures—systolic/diastolic blood pressure (SBP/DBP), height, weight; (3) Lifestyle—smoking, alcohol use, sleep duration; (4) Health conditions—diabetes duration, use of lipid-lowering/antihypertensive medications, stroke or heart disease history (doctor-diagnosed); (5) Mental health—10 item CESD-10 (score 0–30; higher = worse); (6) Laboratory—CRP, HbA1c, eGFR, triglycerides (TG), LDL-C, total cholesterol, HDL-C. SBP/DBP were averaged from three Omron HEM 7200 readings. BMI was categorized by Chinese standards: underweight (< 18.5 kg/m²), normal (18.5–24), overweight (24–28), obese (≥ 28).[ 21 ] eGFR was calculated via the 2012 CKD-EPI equation for diabetics.[ 22 ] 2.6 Diabetes definition Participants were considered diabetic if they met any of the following: (1) Biochemical criteria (2005 ADA): fasting glucose ≥ 126 mg/dL (7 mmol/L), random glucose ≥ 200 mg/dL (11.1 mmol/L), or HbA1c ≥ 6.5%;[ 23 ] (2) Self-reported diagnosis by a doctor; (3) Current use of glucose-lowering treatment. 2.7 Statistical analysis Continuous variables following a normal distribution are presented as mean ± standard deviation (SD), and non-normal variables as median (interquartile range, IQR). Normality was determined by meeting all criteria: absolute skewness 90% of Q–Q plot points within confidence bands. Categorical variables are expressed as frequency (percentage). Group comparisons used independent t tests (normal continuous), Wilcoxon rank sum tests (non-normal continuous), or χ² tests (categorical). Standardized mean differences (|SMD| >0.15) indicated clinically relevant imbalance. Missing baseline and follow up data were imputed using a three-step approach. Categorical variables with ≤ 5 missing values underwent mode imputation. Continuous variables with > 10 valid observations and < 80% missingness were imputed using Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching (five datasets, 10 iterations). Variables with insufficient data were imputed by mean substitution. Missing time series data in Wave 5 were processed using a hybrid approach combining MICE, Kalman filtering, and ensemble reconciliation.[ 24 ] Participants were stratified by study inclusion. Generalized variance inflation factors (GVIF) assessed multicollinearity. Table S1 K means clustering identified participant subgroups; the optimal number of clusters was determined using the elbow method or Silhouette coefficient.[ 25 ] Two clustering strategies were applied: (1) Basic features (BMI, age, diabetes duration, sleep time, CSED, HbA1c, TyG, eGFR, ePWV, HDL, LDL) and (2) Hybrid features (ePWV*TyG, ePWV*BMI, ePWV*HbA1c, ePWV*CRP). Cox proportional hazards models estimated hazard ratios (HRs) with 95% confidence intervals (CIs): Model 1 unadjusted, Model 2 adjusted for demographic, lifestyle, and comorbidity variables, and Model 3 further adjusted for metabolic/laboratory measures. Kaplan–Meier analyses with log rank tests compared CVD free survival across ePWV related quartiles and clusters. Restricted cubic spline Cox models (3–6 knots; lowest Akaike information criterion) tested for non-linear associations. Time dependent ROC curves evaluated predictive accuracy of PWV and interaction terms for 7 year events; AUCs (95% CIs) were calculated via DeLong’s method. Generalized estimating equations (GEE) examined longitudinal associations between ePWV, interaction terms, and cognitive performance (episodic memory, mental intactness, overall score) across four survey waves (2013–2020). We assessed robustness using four sensitivity analyses. (1) E-value quantified the confounding strength needed to nullify associations for continuous and quartile exposures. (2) propensity score matching (PSM) created balanced groups (PP, MAP) via 1:1 nearest-neighbor matching (caliper = 0.2 SD logit PS; SMD < 0.1), followed by re-estimated Cox models for CI risk. (3) Aalen’s additive hazards tested time-varying effects via Supremum, Kolmogorov–Smirnov, and Cramér–von Mises tests. (4) Two-piecewise Cox identified inflection points for ePWV, ePWV*TyG, and ePWV*HbA1c using segmented regression, estimating HRs and 95% CIs below each cut-point in three adjusted models. All analyses were performed using R version 4.4.2. Main R packages involving mice, VIM, car, facroextra, survival, rms, parckwork, timereg, segmented, boot, geepack and broom are used, detailed version information can be found on Table S2 . 3. Results 3.1 Baseline features Among 17,705 eligible adults, 891 entered longitudinal analyses. Table 1 Compared with excluded individuals, included participants were slightly older (median 59 vs. 58 years, SMD 0.05) with longer diabetes duration (4 vs. 3 years, SMD 0.21), more likely urban (18% vs. 10%, SMD 0.22), and better educated (high school/tertiary 45% vs. 33%, SMD 0.25). Smoking prevalence was modestly higher (30% vs. 27%, SMD 0.17), while drinking patterns were similar. Cardiometabolic treatment was more common: lipid-lowering drugs (15% vs. 5%, SMD 0.35) and antihypertensives (41% vs. 24%, SMD 0.37). History of heart disease was more frequent (19% vs. 12%, SMD 0.22); stroke prevalence was low (3% vs. 2%). Adiposity and metabolic markers were less favorable: greater waist circumference (SMD 0.44), BMI (0.45), HbA1c (1.00), TyG (1.29), fasting plasma glucose (1.42), triglycerides (0.56), total cholesterol (0.30), and lower HDL-C (0.42). ePWV was modestly higher (SMD 0.15), while composite indices showed larger group differences: ePWV*TyG (0.62), ePWV*HbA1c (0.84), ePWV*CRP (0.90). After excluding extreme continuous values, 795 and 871 participants were retained for basic (k = 3) and hybrid (k = 2) K-means analyses, respectively. Figure 2 In basic clustering, median ePWV was highest in Cluster 3 (11.40 m/s), defining a high-stiffness phenotype, and lower in Clusters 1–2 (~ 8.8–9.0 m/s). In hybrid clustering, Cluster 1 consistently displayed higher composite stiffness–metabolic indices versus Cluster 2, delineating a metabolically adverse, high-stiffness profile. 3.2 COX analysis Higher arterial stiffness predicted incident cognitive impairment (CI). Table S3 Per-SD ePWV was associated with CI across models: HR 1.44 (95% CI 1.32–1.58) unadjusted, 1.27 (1.06–1.51) Model 2, and 1.32 (1.09–1.58) fully adjusted; corresponding E-values (2.24, 1.85, 1.96) indicated robustness to unmeasured confounding. Composite indices showed similar patterns. ePWV*TyG per SD was significant unadjusted (HR 1.33) and after full adjustment (HR 1.29, P = 0.009 ). ePWV*HbA1c remained significant in all models (HR ~ 1.15–1.27, P ≤ 0.01 ). ePWV*BMI was nonsignificant after adjustment. Quartile analyses supported dose–response relationships, especially for ePWV and ePWV*TyG. For ePWV, Q4 vs. Q1 yielded HR 2.41 unadjusted ( P trend < 0.001 ) with attenuated but consistent direction in Model 3. Cluster-based analyses showed that the high-stiffness basic cluster (Cluster 3) had elevated risk versus Cluster 1 in Model 1 (HR 1.38, P = 0.007 ) but lost significance after adjustment. In hybrid clustering, the lower-risk cluster was protective unadjusted (HR 0.67, P < 0.001 ) but not in Models 2–3, indicating model dependent effects. 3.3 K-M survival analysis Survival curves revealed graded declines in CI-free survival with higher ePWV indices (Fig. 3 ). For ePWV quartiles, 2-year survival decreased from 0.805 (Q1) to 0.656 (Q4). Median survival was 7.0 years for Q3 and 4.0 years for Q4 (log-rank χ² = 55.5, P < 0.001 ). Similar dose–response trends were seen for ePWV*BMI, ePWV*TyG, ePWV*HbA1c, and ePWV*CRP quartiles. Basic clustering identified the high-stiffness group (Cluster 3) with the steepest survival decline—2-, 4-, 7-, and 9-year rates: 0.709, 0.504, 0.369, 0.335—versus intermediate and low-risk clusters ( P < 0.001 ). Hybrid clustering separated a metabolically adverse cluster with lower survival from a lower-risk profile ( P < 0.001 ). 3.4 Restricted cubic spline analysis Test results of k-nodes are shown in Figure S1 and Table S4 . RCS models ( Figure S2 ) showed nonlinear relationships. For ePWV, risk crossed unity at 9.53 m/s and reached HR 1.20 at 10.30 m/s; above 7.5 m/s, each 2.5-unit increase raised risk by ~ 64–98%. ePWV*TyG had its lowest HR (0.50) at 52.1, unity at 90.41, and HR 1.20 at 101.22; per 25-unit increase, risk rose by 50–58%. ePWV*HbA1c’s lowest HR (0.56) occurred at 31.8, unity at ~ 60, and HR 1.20 at 92.05. ePWV*BMI exhibited a W-shaped association, with modest HR changes (1.01–1.68) across plausible ranges. log(ePWV*CRP) showed a U shape, with lowest risk at CRP ~ 2.12 and higher risk at both ends. 3.5 ROC analysis Time dependent ROC curves (Fig. 4 ) evaluated 7-year CI prediction. ePWV had an AUC of 0.685 (95% CI 0.645–0.725); the optimal cut off (9.48 m/s) gave 63.3% sensitivity and 66.1% specificity (Youden = 0.294). ePWV*TyG yielded AUC 0.653, cut-off 91.54, sensitivity 57.6%, specificity 65.2% (Youden = 0.228). ePWV*HbA1c performed lower (AUC 0.607), with cut off 57.33, sensitivity 64%, specificity 53.0% (Youden = 0.170). 3.6 GEE analysis Across four waves, higher ePWV predicted lower cognitive scores after full adjustment. Table 2 Per-SD increases were associated with declines in episodic memory (β = −0.48, P = 0.002 ), mental intactness (β = −0.26, P = 0.038 ), and overall cognition (β = −0.71, P = 0.005 ). Composite measures reinforced these associations. ePWV*TyG per SD was consistently significant across domains (episodic β = −0.39; mental β = −0.27; overall β = −0.64; all P ≤ 0.031 ). ePWVHbA1c showed significance for episodic memory (β = −0.29, P = 0.011 ) and borderline for overall cognition (β = −0.34, P = 0.059 ). Quartile analyses revealed dose–response relationships in minimally adjusted models (Q4 most negative), with attenuation but consistent directionality in fully adjusted models. Table 2 GEE Analysis of ePWV and Cognitive Function Variables (Episodic memory, Mental Intactness, and Overall Score) in Relation to Risk in Diabetic Patients. Episodic memory score Mental intactness score Overall score Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 β(95%CI) P β(95%CI) P β(95%CI) P β(95%CI) P β(95%CI) P β(95%CI) P β(95%CI) P β(95%CI) P β(95%CI) P ePWV -0.86 (-1.02, -0.70) < 0.001 -0.39 (-0.68, -0.10) 0.009 -0.48 (-0.78, -0.17) 0.002 -0.44 (-0.57, -0.31) < 0.001 -0.26 (-0.50, -0.01) 0.037 -0.26 (-0.51, -0.02) 0.038 -1.19 (-1.44, -0.94) < 0.001 -0.64 (-1.11, -0.17) 0.008 -0.71 (-1.21, -0.22) 0.005 ePWV*TyG -0.71 (-0.88, -0.55) < 0.001 -0.23 (-0.46, -0.00) 0.046 -0.39 (-0.70, -0.07) 0.016 -0.39 (-0.52, -0.27) < 0.001 -0.19 (-0.37, -0.01) 0.038 -0.27 (-0.52, -0.02) 0.031 -1.01 (-1.26, -0.77) < 0.001 -0.43 (-0.79, -0.08) 0.017 -0.64 (-1.13, -0.14) 0.012 ePWV*HbA1c -0.56 (-0.73, -0.39) < 0.001 -0.23 (-0.41, -0.04) 0.016 -0.29 (-0.52, -0.07) 0.011 -0.31 (-0.44, -0.18) < 0.001 -0.11 (-0.26, 0.04) 0.144 -0.09 (-0.27, 0.09) 0.341 -0.78 (-1.03, -0.53) < 0.001 -0.31 (-0.60, -0.02) 0.034 -0.34 (-0.69, 0.01) 0.059 ePWV Q1 Ref Ref Ref Q2 -0.28 (-0.74, 0.18) 0.228 0.13 (-0.33, 0.58) 0.583 0.09 (-0.37, 0.54) 0.701 0.04 (-0.33, 0.41) 0.825 0.22 (-0.15, 0.59) 0.24 0.22 (-0.16, 0.60) 0.26 -0.24 (-0.97, 0.49) 0.516 0.26 (-0.46, 0.97) 0.479 0.24 (-0.48, 0.96) 0.518 Q3 -1.15 (-1.65, -0.65) < 0.001 -0.27 (-0.80, 0.27) 0.328 -0.25 (-0.79, 0.28) 0.358 -0.25 (-0.63, 0.13) 0.194 0.15 (-0.29, 0.58) 0.503 0.21 (-0.23, 0.64) 0.357 -1.38 (-2.16, -0.59) < 0.001 -0.26 (-1.12, 0.60) 0.552 -0.18 (-1.04, 0.68) 0.686 Q4 -2.07 (-2.53, -1.60) < 0.001 -0.39 (-1.09, 0.30) 0.267 -0.48 (-1.20, 0.24) 0.191 -0.98 (-1.36, -0.61) < 0.001 -0.14 (-0.72, 0.45) 0.648 -0.09 (-0.69, 0.51) 0.771 -2.76 (-3.50, -2.02) < 0.001 -0.52 (-1.65, 0.61) 0.366 -0.52 (-1.69, 0.65) 0.383 ePWV*TyG Q1 Ref Ref Ref Q2 -0.63 (-1.13, -0.14) 0.012 -0.29 (-0.76, 0.18) 0.222 -0.33 (-0.79, 0.13) 0.164 -0.19 (-0.56, 0.19) 0.324 -0.11 (-0.48, 0.26) 0.556 -0.10 (-0.49, 0.29) 0.624 -0.58 (-1.33, 0.18) 0.133 -0.31 (-1.03, 0.40) 0.391 -0.31 (-1.05, 0.44) 0.418 Q3 -1.29 (-1.76, -0.81) < 0.001 -0.60 (-1.07, -0.12) 0.013 -0.63 (-1.11, -0.15) 0.011 -0.38 (-0.75, -0.00) 0.048 -0.21 (-0.59, 0.18) 0.298 -0.17 (-0.60, 0.27) 0.449 -1.10 (-1.84, -0.35) 0.004 -0.53 (-1.28, 0.22) 0.165 -0.52 (-1.35, 0.31) 0.217 Q4 -1.40 (-1.87, -0.93) < 0.001 -0.53 (-1.04, -0.02) 0.042 -0.62 (-1.19, -0.04) 0.035 -0.99 (-1.36, -0.63) < 0.001 -0.41 (-0.89, 0.07) 0.095 -0.43 (-1.02, 0.15) 0.148 -2.68 (-3.40, -1.96) < 0.001 -1.06 (-1.99, -0.13) 0.025 -1.23 (-2.35, -0.11) 0.032 ePWV*HbA1c Q1 Ref Ref Ref Q2 -0.47 (-0.95, 0.00) 0.051 -0.21 (-0.66, 0.24) 0.361 -0.25 (-0.71, 0.21) 0.283 -0.12 (-0.49, 0.26) 0.547 0.04 (-0.33, 0.41) 0.833 0.06 (-0.32, 0.43) 0.771 -0.60 (-1.37, 0.17) 0.13 -0.18 (-0.91, 0.55) 0.631 -0.19 (-0.92, 0.55) 0.614 Q3 -0.76 (-1.23, -0.28) 0.002 -0.21 (-0.68, 0.26) 0.387 -0.25 (-0.76, 0.27) 0.349 -0.31 (-0.68, 0.06) 0.105 0.05 (-0.33, 0.43) 0.786 0.11 (-0.28, 0.50) 0.584 -1.43 (-2.18, -0.67) < 0.001 -0.50 (-1.25, 0.24) 0.184 -0.47 (-1.24, 0.29) 0.226 Q4 -1.94 (-2.40, -1.48) < 0.001 -0.64 (-1.22, -0.06) 0.031 -0.84 (-1.53, -0.15) 0.017 -0.73 (-1.12, -0.35) < 0.001 -0.19 (-0.63, 0.25) 0.389 -0.11 (-0.60, 0.37) 0.652 -1.85 (-2.61, -1.10) < 0.001 -0.61 (-1.45, 0.22) 0.15 -0.60 (-1.53, 0.34) 0.209 3.7 Sensitivity analysis We performed four sensitivity analyses: E-value, PSM-balanced Cox, Aalen’s additive hazards, and two-piecewise Cox regression. After PSM, ePWV, ePWV*TyG, and ePWV*CRP remained significantly associated with CI. Table S5 Aalen’s models showed good fit and stability after full adjustment. Table S6 Two-piecewise Cox indicated protective effects for low ePWV (< 8.48 m/s) and ePWV*HbA1c (< 99.03), with ePWV*HbA1c (< 119.4) remaining significant after full adjustment (HR = 0.591, P = 0.029 ). Table S7 , Figure S3 4. Discussion In this large, nationally representative cohort of Chinese adults with diabetes, we found that higher ePWV—an accessible surrogate for arterial stiffness—was significantly associated with an increased risk of incident CI over a median follow-up of seven years. Associations were robust to extensive adjustment for sociodemographic, lifestyle, and clinical variables, with HR of ~ 1.3 per SD increase and ~ 1.2 per 1 m/s increase in ePWV. Composite indices integrating ePWV with metabolic measures—particularly the TyG index and HbA1c—demonstrated comparable or stronger effects. Both quartile-based and RCS analyses supported clear dose–response relationships, and Kaplan–Meier curves showed substantially lower event-free survival in participants with higher ePWV or composite scores. Sensitivity analyses, including PSM, yielded similar findings. Subgroup analysis indicated effect modification by education level and BMI category. Prior studies have identified elevated ePWV as a strong predictor of cardiovascular morbidity, stroke, and all-cause mortality, independent of traditional risk factors.[ 26 – 30 ] Effect sizes for composite cardiovascular outcomes are comparable to those of gold standard cfPWV.[ 8 ] Although the link between ePWV and cerebral small vessel disease (CSVD) is less studied, evidence is emerging. Higher ePWV has been associated with silent lacunar infarcts[ 31 ] and increased white matter hyperintensity (WMH) volume, even after vascular risk adjustment.[ 32 ] WMHs, in turn, are linked to a two-fold higher dementia risk and three-fold higher stroke risk.[ 33 ] Our results align with work in general populations showing arterial stiffness as an independent dementia risk factor. For example, one large cohort study found that each 1 m/s rise in ePWV was linked to adjusted HRs of 1.51 for all-cause dementia, 1.58 for AD, and 1.30 for VaD.[ 34 ] These effect sizes were moderately higher than ours (HR = 1.20 per 1 m/s). The study also reported that highest-tertile ePWV (> 12.71 m/s) conveyed markedly elevated risk versus the lowest tertile (≤ 11.65 m/s), particularly for AD (HR 3.92). [ 34 ] Our quartiles differed (≤ 8.63, 8.63–9.54, 9.54–10.78, ≥ 10.78 m/s). Unadjusted models showed a strong gradient: Q3 and Q4 vs. Q1 corresponded to HR 1.81 and 2.41, respectively, with P 10 m/s as “high risk,”[ 35 , 36 ] our two-piece regression indicated an inflection at ~ 8.48 m/s, suggesting that harmful cerebral effects may manifest earlier—a notion consistent with cfPWV evidence where 8.5 m/s was associated with impaired white matter microstructure.[ 37 ] Modeling ePWV continuously showed significant, independent associations with reduced scores in episodic memory (β = − 0.48), mental intactness (β = − 0.26), and overall cognition (β = − 0.71). These findings extend a substantial body of longitudinal and cross-sectional evidence linking greater arterial stiffness to cognitive decline. In the Whitehall II Imaging Sub study conducted in the UK, baseline measures showing higher cfPWV were linked to subsequent deficits in semantic fluency (B = − 0.47, 95% CI: − 0.76 to − 0.18; P < 0.007) and verbal learning (B = − 0.36, 95% CI: − 0.60 to − 0.12; P < 0.007).[ 38 ]The Toledo Study for Healthy Aging in Spain reported comparable trends, noting that greater cfPWV was tied to weaker memory performance and faster declines in both total recall and free recall over the observation period.[ 39 ] Parallel evidence from cross sectional analyses reinforces these associations. In NHANES, elevated ePWV correlated negatively with executive function; for non-Hispanic Black adults, this was reflected in reduced Digit Symbol Substitution Test scores (β = − 3.47, 95% CI: − 3.90 to − 3.00; P < 0.001) after controlling for multiple confounders.[ 40 ] Findings from the Northern Manhattan Study also revealed that individuals with higher ePWV (mean ± SD: 11 ± 2 m/s) presented with poorer global cognition at baseline (β = − 0.100, 95% CI: − 0.120 to − 0.080) and experienced more rapid deterioration in processing speed, episodic and semantic memory, and executive function (β = − 0.063, 95% CI: − 0.082 to − 0.045).[ 41 ] Several vascular–metabolic pathways may explain our findings. Increased arterial stiffness raises central pulse pressure and accelerates waveform transmission, causing mechanical damage to cerebral microvessels.[ 37 ] This impairs vasoreactivity and promotes chronic hypoperfusion, preferentially affecting hippocampal and white matter regions—key substrates for memory and executive function.[ 37 ] In diabetes, hyperglycemia, insulin resistance, and dyslipidemia accelerate vascular remodeling, endothelial dysfunction, and low-grade inflammation, compounding the deleterious effects of mechanical vascular load.[ 42 ] To our knowledge, this is the first large-scale study to jointly examine four metabolic dimensions in interaction with ePWV—TyG index, HbA1c, BMI, and CRP—in relation to cognitive performance. All four metrics emerged as independent predictors of cognitive decline, irrespective of diabetes status. TyG, a validated surrogate for insulin resistance, has been associated with dementia risk in meta-analyses (OR = 1.14, 95% CI 1.12–1.16).[ 15 ] HbA1c–cognition associations remain inconsistent: a pooled analysis of randomized trials found only modest slowing of cognitive decline at levels > 7.0%,[ 43 ] and both the ADVANCE and VADT trials reported no cognitive benefit from intensive glucose lowering (< 6.0–6.5%).[ 44 , 45 ] Elevated CRP, a marker of chronic inflammation, has been linked to impaired cerebrovascular reactivity and subsequent executive dysfunction in diabetes.[ 13 ] Similarly, obesity exacerbates central insulin resistance, promoting tau pathology, neurofibrillary tangle formation, and neuronal loss. [ 14 ] From a clinical standpoint, the relationship between T2DM and dementia is bidirectional: cognitive impairment increases vulnerability to severe hypoglycemic events, cardiovascular disease, stroke, and premature mortality.[ 46 ] Individuals with both conditions are also at greater risk of acute hyperglycemic crises—such as diabetic ketoacidosis and hyperglycemic hyperosmolar state—than those without dementia, reflecting the compounded challenges of managing diabetes in the context of declining cognition and reduced self-care capacity.[ 46 ] Early identification and intervention during this critical window may help slow the progression of cognitive decline and reduce the future burden of dementia.[ 47 ] Given that ePWV can be readily estimated from routinely collected clinical data, it offers a practical, low-cost, and noninvasive approach for identifying high-risk individuals and potentially enabling self-monitoring in at-risk populations. Integrating vascular stiffness assessment into diabetes care could therefore provide a dual benefit: optimizing cardiometabolic health while preserving cognitive function. Our results have several implications. First, ePWV is easily calculated from age and blood pressure, enabling widespread, low-cost assessment without the need for specialized tonometry or imaging. The identified threshold of approximately 8.5 m/s for 7-year risk discrimination may facilitate earlier identification of individuals warranting closer cognitive monitoring or preventive interventions, though external validation is needed. Second, pairing ePWV with routine metabolic markers such as TyG or HbA1c may enhance early risk detection in diabetes, allowing for finer personalization of vascular–metabolic risk control. Third, the observed modifying effects of education and BMI suggest that preventive strategies should be tailored, with particular attention to overweight individuals and those with any educational attainment background, who may exhibit heightened vulnerability. Strengths of this study include the use of a large, nationwide diabetes cohort, prospective design, and the application of multiple complementary statistical approaches—Cox regression, Kaplan–Meier survival analysis, RCS, clustering, ROC analysis, and extensive sensitivity testing—to confirm findings. Nonetheless, several limitations merit consideration. First, while ePWV has been shown to predict cardiovascular disease in healthy populations, its prognostic utility in diabetes remains less certain. In this high-risk group, the relationship between vascular stiffness and adverse outcomes may be reciprocal, potentially diminishing ePWV’s ability to act as an independent predictor after accounting for coexisting metabolic and vascular abnormalities. This warrants further validation in longitudinal studies specifically designed for diabetic cohorts. Moreover, as an estimated measure, ePWV may be less precise than direct carotid–femoral PWV assessment, although its feasibility and applicability in large-scale epidemiological surveys make it a pragmatic alternative.[ 48 ] Second, cognitive outcomes were assessed via standard survey instruments rather than detailed neuropsychological batteries, potentially leading to misclassification. Third, as an observational study, causal inference is limited and residual confounding cannot be completely excluded despite robustness checks. Fourth, clustering analyses did not yield robust independent effects after full adjustment, suggesting that more sophisticated machine-learning phenotyping or additional features may be needed to capture clinically meaningful subgroups. Finally, generalizability beyond Chinese adults with diabetes is uncertain and should be examined in diverse populations. 5. Conclusion In summary, in a large prospective cohort of adults with diabetes, higher arterial stiffness, as measured by ePWV, and composite indices combining stiffness with metabolic measures were independently associated with greater risk of cognitive impairment. These associations were dose–responsive, robust across analytic methods, and modified by certain demographic and anthropometric factors. ePWV and its composites may represent practical, scalable tools for early identification of individuals at heightened risk of cognitive decline in the diabetic population, offering opportunities for timely intervention Declarations 6. Acknowledgements The authors thank the CHARLS research team and all study participants for their contributions. This research was based on data from the CHARLS database. 7. Author contributions Lingjie Kong, Guangning Zhang, and Lingyuan Wu contributed to the conception and design of the manuscript; Lingjie Kong contributed to the acquisition and analysis of data; Lingjie Kong, Guangning Zhang, Jun Wu, Lingyuan Wu, Xizhen Zhou, and Linfei Wang contributed to the drafting the text. Lingjie Kong, Xizheng Zhou, and Linfei Wang prepared the figures. All authros reviewed the manuscript. 8. Funding Medical and Health Technology Project of Hangzhou (grant. A20220601), Zhejiang Provincal Medical and Health Technology project(grant.2021KY885) 9. Availability of data and materials The datasets generated during and/or used during the current study are available from the corresponding author on reasonable request. Related R code is available on first author by request. 10. Ethics approval and consent to participate CHARLS was approved by the Institutional Review Board of Peking University in accordance with the Declaration of Helsinki (approval number: IRB00001052-11015 for the household survey and IRB00001052-11014 for blood samples), and all participants provided //written informed consent. 11. Competing interests The authors declare no competing interests. 12. Consent for publication Not applicable References Hebert LE, Weuve J, Scherr PA, et al. Alzheimer disease in the United States (2010–2050) estimated using the 2010 census. Neurology. 2013;80:1778–83. Xue M, Xu W, Ou YN, et al. Diabetes mellitus and risks of cognitive impairment and dementia: A systematic review and meta-analysis of 144 prospective studies. Ageing Res Rev. 2019;55:100944. Meng X, Zhang H, Zhao Z, et al. Type 3 diabetes and metabolic reprogramming of brain neurons: causes and therapeutic strategies. Mol Med. 2025;31:61. Hassing LB, Hofer SM, Nilsson SE, et al. Comorbid type 2 diabetes mellitus and hypertension exacerbates cognitive decline: evidence from a longitudinal study. Age Ageing. 2004;33:355–61. Tian X, Zuo Y, Chen S, et al. Hypertension, Arterial Stiffness, and Diabetes: a Prospective Cohort Study. Hypertension. 2022;79:1487–96. Boutouyrie P, Chowienczyk P, Humphrey JD, et al. Arterial Stiffness and Cardiovascular Risk in Hypertension. Circ Res. 2021;128:864–86. Laurent S, Cockcroft J, Van Bortel L, et al. Expert consensus document on arterial stiffness: methodological issues and clinical applications. Eur Heart J. 2006;27:2588–605. Greve SV, Blicher MK, Kruger R, et al. Estimated carotid-femoral pulse wave velocity has similar predictive value as measured carotid-femoral pulse wave velocity. J Hypertens. 2016;34:1279–89. Liu Y, Xu K, Wu S, et al. Value of estimated pulse wave velocity to identify left ventricular hypertrophy prevalence: insights from a general population. BMC Cardiovasc Disord. 2022;22:157. Muhammad IF, Borne Y, Ostling G, et al. Arterial Stiffness and Incidence of Diabetes: A Population-Based Cohort Study. Diabetes Care. 2017;40:1739–45. Ferreira MT, Leite NC, Cardoso CR, et al. Correlates of aortic stiffness progression in patients with type 2 diabetes: importance of glycemic control: the Rio de Janeiro type 2 diabetes cohort study. Diabetes Care. 2015;38:897–904. Gorska-Ciebiada M, Saryusz-Wolska M, Borkowska A, et al. C-Reactive Protein, Advanced Glycation End Products, and Their Receptor in Type 2 Diabetic, Elderly Patients with Mild Cognitive Impairment. Front Aging Neurosci. 2015;7:209. Chung CC, Pimentel D, Jor'dan AJ, et al. Inflammation-associated declines in cerebral vasoreactivity and cognition in type 2 diabetes. Neurology. 2015;85:450–8. Selman A, Burns S, Reddy AP et al. The Role of Obesity and Diabetes in Dementia. Int J Mol Sci 23 (2022). Nayak SS, Kuriyakose D, Polisetty LD, et al. Diagnostic and prognostic value of triglyceride glucose index: a comprehensive evaluation of meta-analysis. Cardiovasc Diabetol. 2024;23:310. Zhao Y, Hu Y, Smith JP, et al. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43:61–8. Luo L, Feng W, Mei M, et al. Greater variability in HDL-C was associated with an increased risk of cognitive decline in the middle- and elderly Chinese: A cohort study. Arch Gerontol Geriatr. 2024;125:105503. Crimmins EM, Kim JK, Langa KM, et al. Assessment of cognition using surveys and neuropsychological assessment: the Health and Retirement Study and the Aging, Demographics, and Memory Study. J Gerontol B Psychol Sci Soc Sci. 2011;66(1):i162–71. Liu H, Zou L, Zhou R, et al. Long-Term Increase in Cholesterol Is Associated With Better Cognitive Function: Evidence From a Longitudinal Study. Front Aging Neurosci. 2021;13:691423. Xiao Y, Hong X, Neelagar R, et al. Association between glycated hemoglobin A1c levels, control status, and cognitive function in type 2 diabetes: a prospective cohort study. Sci Rep. 2025;15:5011. He W, Li Q, Yang M, et al. Lower BMI cutoffs to define overweight and obesity in China. Obes (Silver Spring). 2015;23:684–91. Johnson DW, Jones GR, Mathew TH, et al. Chronic kidney disease and automatic reporting of estimated glomerular filtration rate: new developments and revised recommendations. Med J Aust. 2012;197:224–5. Refardt J. Diagnosis and differential diagnosis of diabetes insipidus: Update. Best Pract Res Clin Endocrinol Metab. 2020;34:101398. Azur MJ, Stuart EA, Frangakis C, et al. Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res. 2011;20:40–9. Huang JZ, Ng MK, Rong H, et al. Automated variable weighting in k-means type clustering. IEEE Trans Pattern Anal Mach Intell. 2005;27:657–68. Heffernan KS, Jae SY, Loprinzi PD. Association Between Estimated Pulse Wave Velocity and Mortality in U.S. Adults. J Am Coll Cardiol. 2020;75:1862–4. Vlachopoulos C, Terentes-Printzios D, Laurent S, et al. Association of Estimated Pulse Wave Velocity With Survival: A Secondary Analysis of SPRINT. JAMA Netw Open. 2019;2:e1912831. Jae SY, Heffernan KS, Kurl S, et al. Association between estimated pulse wave velocity and the risk of stroke in middle-aged men. Int J Stroke. 2021;16:551–5. Chen C, Bao W, Chen C, et al. Association between estimated pulse wave velocity and all-cause mortality in patients with coronary artery disease: a cohort study from NHANES 2005–2008. BMC Cardiovasc Disord. 2023;23:412. Chen T, Yin H, Zhou Y et al. Relationship between estimated pulse wave velocity trajectories and cardiovascular disease risk in patients with cardiovascular-kidney-metabolic syndrome stages 0–3. Nutr Metab Cardiovasc Dis (2025) 104192. Zhou Y, Zhang Y, Xu G, et al. Association between estimated pulse wave velocity and silent lacunar infarct in a Korean population. Front Cardiovasc Med. 2023;10:1070997. Ariko TA, Aimagambetova B, Gardener H, et al. Estimated Pulse-Wave Velocity and Magnetic Resonance Imaging Markers of Cerebral Small-Vessel Disease in the NOMAS. J Am Heart Assoc. 2024;13:e035691. Wardlaw JM, Valdes Hernandez MC, Munoz-Maniega S. What are white matter hyperintensities made of? Relevance to vascular cognitive impairment. J Am Heart Assoc. 2015;4:001140. Li J, Ren Y, Wang L, et al. Estimated pulse wave velocity associated with cognitive phenotypes in a rural older population in China: A cohort study. Alzheimers Dement. 2025;21:e14491. Jae SY, Heffernan KS, Kim HJ, et al. Impact of estimated pulse wave velocity and socioeconomic status on the risk of stroke in men: a prospective cohort study. J Hypertens. 2022;40:1165–9. Hsu PC, Lee WH, Tsai WC, et al. Usefulness of Estimated Pulse Wave Velocity in Prediction of Cardiovascular Mortality in Patients With Acute Myocardial Infarction. Am J Med Sci. 2021;361:479–84. Badji A, Noriega A, de la Colina A, Karakuzu, et al. Arterial stiffness cut-off value and white matter integrity in the elderly. Neuroimage Clin. 2020;26:102007. Suri S, Chiesa ST, Zsoldos E, et al. Associations between arterial stiffening and brain structure, perfusion, and cognition in the Whitehall II Imaging Sub-study: A retrospective cohort study. PLoS Med. 2020;17:e1003467. Bareiro FAQ, Carnicero JA, Acha AA, et al. Carotid-femoral pulse wave velocity score, an estimator of cognitive performance in the elderly: results from the Toledo Study for Healthy Aging. Geroscience. 2024;46:5711–23. Heffernan KS, Stoner L, Meyer ML, et al. Association Between Estimated Pulse Wave Velocity and Cognitive Performance in Older Black and White Adults in NHANES. J Alzheimers Dis. 2022;88:985–93. Aimagambetova B, Ariko T, Gardener H, et al. Association of estimated pulse wave velocity with cognitive function in a multiethnic diverse population: The Northern Manhattan Study. Alzheimers Dement. 2024;20:4903–13. Ehtewish H, Arredouani A, El-Agnaf O. Diagnostic, Prognostic, and Mechanistic Biomarkers of Diabetes Mellitus-Associated Cognitive Decline. Int J Mol Sci 23 (2022). Tang X, Cardoso MA, Yang J, et al. Impact of Intensive Glucose Control on Brain Health: Meta-Analysis of Cumulative Data from 16,584 Patients with Type 2 Diabetes Mellitus. Diabetes Ther. 2021;12:765–79. Cukierman-Yaffe T, Gerstein HC, Williamson JD, et al. Relationship between baseline glycemic control and cognitive function in individuals with type 2 diabetes and other cardiovascular risk factors: the action to control cardiovascular risk in diabetes-memory in diabetes (ACCORD-MIND) trial. Diabetes Care. 2009;32:221–6. Launer LJ, Miller ME, Williamson JD, et al. Effects of intensive glucose lowering on brain structure and function in people with type 2 diabetes (ACCORD MIND): a randomised open-label substudy. Lancet Neurol. 2011;10:969–77. American Diabetes Association Professional, Practice C. Comprehensive Medical Evaluation and Assessment of Comorbidities: Standards of Care in Diabetes-2025. Diabetes Care. 2025;48:S59–85. 4. Kale M, Wankhede N, Pawar R, et al. AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling. Ageing Res Rev. 2024;101:102497. Greve SV, Laurent S, Olsen MH. Estimated Pulse Wave Velocity Calculated from Age and Mean Arterial Blood Pressure. Pulse (Basel). 2017;4:175–9. Additional Declarations No competing interests reported. Supplementary Files TableS1.doc TableS2.doc TableS4.doc TableS5.doc TableS7.doc TableS6.doc TableS3.doc FigureS2.docx FigureS1.docx FigureS3.docx Graphicabstract.jpg Cite Share Download PDF Status: Published Journal Publication published 30 Mar, 2026 Read the published version in BMC Endocrine Disorders → Version 1 posted Editorial decision: Revision requested 27 Nov, 2025 Reviews received at journal 27 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers invited by journal 21 Oct, 2025 Editor assigned by journal 19 Oct, 2025 Submission checks completed at journal 19 Oct, 2025 First submitted to journal 11 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7835974","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538249112,"identity":"246b0490-e83a-46b1-94bc-16d11c0cb51c","order_by":0,"name":"Lingjie Kong","email":"","orcid":"","institution":"Peking Union medical college Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Lingjie","middleName":"","lastName":"Kong","suffix":""},{"id":538249113,"identity":"13644733-52f5-45fb-81d5-6966dc5fc7dc","order_by":1,"name":"Guangning Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Jiaxing University","correspondingAuthor":false,"prefix":"","firstName":"Guangning","middleName":"","lastName":"Zhang","suffix":""},{"id":538249114,"identity":"49392f19-1b8b-4de2-9a84-a73b0b0a1998","order_by":2,"name":"Jun Wu","email":"","orcid":"","institution":"Affiliated Hospital of Jiaxing University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Wu","suffix":""},{"id":538249115,"identity":"e328fc48-fe98-4412-a9f0-971f47ed71e4","order_by":3,"name":"Xizhen Zhou","email":"","orcid":"","institution":"Westlake University","correspondingAuthor":false,"prefix":"","firstName":"Xizhen","middleName":"","lastName":"Zhou","suffix":""},{"id":538249116,"identity":"afec38c9-fc07-464e-b667-1021436a99fd","order_by":4,"name":"Linfei Wang","email":"","orcid":"","institution":"Westlake University","correspondingAuthor":false,"prefix":"","firstName":"Linfei","middleName":"","lastName":"Wang","suffix":""},{"id":538249117,"identity":"2be29133-40f2-4ecc-8819-d016ab6fabc4","order_by":5,"name":"Lingyuan Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYFAC5oYDUMbBBwkGEnL8zMyHH+DXwgjTwpZs8KDCwliynS3NgJAWKIPHTPDBmYrEDed5FCTwaTC4kdh4uODX4cT+2Q1mDIltEombD/MwGDDU2ETj0dJweGbf4cQZdw6kPQBqMd52mPfAA4ZjabkNOLSYgbTw9hxObLiRcNwAqEV222G+BAPGhsOEtcy/AXJVmwTj5mYeAwmCWnh+HE7ccCOZTSLhjITiBmYCWuzPPATa0pBuvPFGGrNBQoWEscRhYCAn4PGLZHvy4c88f6xl593I//jwh0GdHH//4cMPPtTY4NQCBoxtzWgiCfiUg8GfOoJKRsEoGAWjYAQDACbYaruwP6MXAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Jiaxing University","correspondingAuthor":true,"prefix":"","firstName":"Lingyuan","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-10-11 14:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7835974/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7835974/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12902-026-02247-5","type":"published","date":"2026-03-30T15:57:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":95221412,"identity":"9aba704e-47e4-4d13-8940-b343d77e839c","added_by":"auto","created_at":"2025-11-05 16:18:57","extension":"jpg","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":252284,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/03d4933cde345ca53556e4a7.jpg"},{"id":95220849,"identity":"1a13e0a5-08b1-405f-b96a-74407db9e875","added_by":"auto","created_at":"2025-11-05 16:16:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":46451,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/f480f5d81bd061773cb59982.docx"},{"id":95007280,"identity":"0c034277-0074-42e9-a5da-00fdccc545f6","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":530264,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/4860305d9ff1b5d0805d0b2c.jpg"},{"id":95221488,"identity":"4497ecf5-11e4-41e6-b9b9-bb7ba3f6aa39","added_by":"auto","created_at":"2025-11-05 16:19:08","extension":"doc","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1839772,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/51f5d0b1d467c161362d7d8b.doc"},{"id":95007301,"identity":"e643b25c-fb62-4645-9f65-317653ac1133","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":794951,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/d5b71df4b16aeb6f5601cb31.jpg"},{"id":95220923,"identity":"d8165a29-a551-4434-a341-0fd02d7c147e","added_by":"auto","created_at":"2025-11-05 16:17:10","extension":"doc","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1925955,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/3e52af39fc026661cca6be65.doc"},{"id":95007285,"identity":"1d3fc0fa-f407-4452-bf0a-e2e53bfb40b0","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112376,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/1ec8263aec138de02fbf2941.jpg"},{"id":95007283,"identity":"21b9bb81-4095-4aee-92d5-0b358a811235","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"json","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7750,"visible":true,"origin":"","legend":"","description":"","filename":"370b03b695eb447898d57ac56b07655a.json","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/f6e4aff657cdffc7d3183a4b.json"},{"id":95221008,"identity":"9d2c6283-0c74-4be7-9a4e-9507dac9b804","added_by":"auto","created_at":"2025-11-05 16:17:49","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":103526,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/5eb15b515aa3d42ec6c17872.docx"},{"id":95221146,"identity":"34cc8250-b04d-47e6-90ae-9521db23b0aa","added_by":"auto","created_at":"2025-11-05 16:18:27","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":194032,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/841698ad2402aeed5585ad85.docx"},{"id":95221392,"identity":"2a470ac2-96a5-4b2d-aae3-cf984d6a21bb","added_by":"auto","created_at":"2025-11-05 16:18:55","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122710,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/ba19d717b437cc9a60858592.docx"},{"id":95220891,"identity":"4f4ae5cb-2212-43e7-b527-9ed9b3dad918","added_by":"auto","created_at":"2025-11-05 16:16:46","extension":"jpg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2070591,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicabstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/ea049626506346c24accbe93.jpg"},{"id":95221421,"identity":"f5cd55ad-c958-41f3-9312-e9df8a248220","added_by":"auto","created_at":"2025-11-05 16:18:59","extension":"doc","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22123,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/5e5b05d1db55208f8e1c1148.doc"},{"id":95221068,"identity":"da9db740-838e-4885-87e1-20a0a4826910","added_by":"auto","created_at":"2025-11-05 16:18:10","extension":"doc","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":18457,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/9f67ac82e927a326cb89e616.doc"},{"id":95007303,"identity":"90088334-a185-45ed-9586-34a7be89f7cc","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"doc","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1838885,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/d2b254ea5b5d0f5f60b495c9.doc"},{"id":95007312,"identity":"97634e8a-8bd1-4a72-a0e7-49fa8aa94a46","added_by":"auto","created_at":"2025-11-03 09:39:21","extension":"doc","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1718858,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/31df4bcd6a58f1a44cd82d92.doc"},{"id":95007308,"identity":"991fe4d3-4122-4f5a-b050-c9596d5152ba","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"doc","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1830008,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/acf91f21d3ffd680495f6276.doc"},{"id":95220893,"identity":"ea986f1e-ed3a-4040-a48f-831a13e36573","added_by":"auto","created_at":"2025-11-05 16:16:47","extension":"doc","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1836719,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/522944b1e3edfba2514380ab.doc"},{"id":95007315,"identity":"40eea7cc-11cd-4bb7-9074-baba10c57901","added_by":"auto","created_at":"2025-11-03 09:39:21","extension":"doc","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1822950,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/72a1c0538db8f862e6ed8bd3.doc"},{"id":95220995,"identity":"c311b862-ebf1-4fff-8ff6-42bc9eb08a08","added_by":"auto","created_at":"2025-11-05 16:17:43","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":164437,"visible":true,"origin":"","legend":"","description":"","filename":"370b03b695eb447898d57ac56b07655a1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/a40daf9c411aa015a435a6cc.xml"},{"id":95221387,"identity":"f3e4f043-f3c3-4550-b8a1-0175717fdbf5","added_by":"auto","created_at":"2025-11-05 16:18:53","extension":"jpg","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":252284,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/091f94c1c37353203988c874.jpg"},{"id":95221505,"identity":"4214c6a8-3bdd-47f4-8c0c-a937e852a8f9","added_by":"auto","created_at":"2025-11-05 16:19:12","extension":"jpg","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":530264,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/536484103d193cf70298464d.jpg"},{"id":95221131,"identity":"caf14d4b-5a8a-428d-baab-4ea2b09df880","added_by":"auto","created_at":"2025-11-05 16:18:24","extension":"jpg","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":794951,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/20dea24af9947e599625dcf2.jpg"},{"id":95007310,"identity":"9b39426f-215f-4045-b2d0-78577d5fa12b","added_by":"auto","created_at":"2025-11-03 09:39:21","extension":"jpg","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112376,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/fdf0b3ec5f9fb31b9c1b0951.jpg"},{"id":95221362,"identity":"77b29248-2b58-4834-92d6-e1caa2a3f5e4","added_by":"auto","created_at":"2025-11-05 16:18:51","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":139760,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/f74e1456cdf75a3d96ce0f3a.png"},{"id":95007305,"identity":"fbb58f28-e8b9-45c6-a52a-700e65998ff8","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":214980,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/fdda913a40b7ac14ae8a9603.png"},{"id":95007318,"identity":"5133cb38-8b59-4f2c-986b-6d73b5dddfcf","added_by":"auto","created_at":"2025-11-03 09:39:21","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":192273,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/940cfaab716905ca97f1cd6e.png"},{"id":95007306,"identity":"ff689750-8bec-4d14-9216-1418b850d960","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":46033,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/8a0f36c87af02d3b8e99e968.png"},{"id":95007313,"identity":"33d90742-bdd3-4a55-983f-618d1d9a1610","added_by":"auto","created_at":"2025-11-03 09:39:21","extension":"xml","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":162064,"visible":true,"origin":"","legend":"","description":"","filename":"370b03b695eb447898d57ac56b07655a1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/269fa64a6f1570e557b6c493.xml"},{"id":95221414,"identity":"ca486f0c-c68e-46b4-9bf4-16f9e8bea996","added_by":"auto","created_at":"2025-11-05 16:18:57","extension":"html","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":175268,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/e2e20e9f464fbc0000216407.html"},{"id":95007276,"identity":"3d7a0342-0696-4a06-9deb-d0d2733a48ef","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":252284,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart.\u003c/strong\u003e ePWV, estimated pulse wave velocity; RCS-COX analysis, restricted cubic spline Cox proportional hazards regression analysis; ROC analysis, receiver operating characteristic curve analysis; GEE analysis, generalized estimating equations analysis. *, K-Mean clustering involves BMI, age, time since duration, night sleeping time, CSED, HbA1c, TyG, eGFR, ePWV, HDL and LDL; ^, K-Mean clustering involves ePWV*TyG, ePWV*BMI, ePWV*HbA1c and log(ePWV*CRP).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/f86d0a47eebeb54f0cf76582.jpg"},{"id":95221177,"identity":"12722be7-5944-4881-b883-3017c53d23b0","added_by":"auto","created_at":"2025-11-05 16:18:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":530264,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eK-Means clustering.\u003c/strong\u003e 1) Optimal k was identified using elbow rule or Silhouette coefficient; 2) Clustering plot; 3) The distribution of involved features in different clusters.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/1f2003a353aed78b7f973953.jpg"},{"id":95007281,"identity":"ea6ff7f9-7cbb-4867-80c6-37e1410475eb","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":794951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curves.\u003c/strong\u003e A) ePWV quartiles; B) ePWV*TyG quartiles; C) Epwv*BMI quartiles; D) ePWV*CRP quartiles; E) ePWV*HbA1c quartiles; F) Clusters in basic K-Mean clustering; G) Clusters in hybrid K-Mean clustering.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/b9894f6f3fc16a3c0107061c.jpg"},{"id":95221122,"identity":"7bfade45-c6ff-4b67-935e-384cab28916e","added_by":"auto","created_at":"2025-11-05 16:18:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":112376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC analysis. \u003c/strong\u003eePWV, estimated pulse wave velocity; TyG, Triglyceride-Glucose Index.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/e4bdd6f7214096c4a31a3cf8.jpg"},{"id":106345038,"identity":"0929ab6b-775b-480e-8c55-3591351bab86","added_by":"auto","created_at":"2026-04-07 16:17:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2838642,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/cd487e99-a919-4b85-b206-11ed3a7fcd43.pdf"},{"id":95007273,"identity":"061f7c08-0aa0-4f1c-8b61-7d40178c056a","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22123,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/74ae3573299feeac37f50c89.doc"},{"id":95007274,"identity":"511003ea-3040-445b-a383-2f5e876c9e96","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18457,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/5c7ea70c5f389dcf8dab73d3.doc"},{"id":95007279,"identity":"0bdd04df-0729-4d0f-988f-a1f274018040","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"doc","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1718858,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/8d9686d346497d86fe03e013.doc"},{"id":95221154,"identity":"8d2a110c-cc66-492f-8da4-0b36581b5d9e","added_by":"auto","created_at":"2025-11-05 16:18:30","extension":"doc","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1830008,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/733efc08e5a4c9c647783758.doc"},{"id":95007289,"identity":"e2345855-f34e-4c18-9e53-1a20b40ae755","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"doc","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1822950,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/1a872537d669233877dcf2f0.doc"},{"id":95221032,"identity":"78a47fbf-93da-4526-9f4a-45f8d2e23ad9","added_by":"auto","created_at":"2025-11-05 16:18:01","extension":"doc","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1836719,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/f0578b9369099172bc0e0a6d.doc"},{"id":95007296,"identity":"852ea99f-ddb3-4cf9-9e14-26de3526de4d","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"doc","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1838885,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.doc","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/ac6a44f178ea2bf52176b88b.doc"},{"id":95007298,"identity":"d3e022ce-ee94-42ee-948c-f3a39553b4e8","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":194032,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/f90896c2430bde4cc6e04e1c.docx"},{"id":95007291,"identity":"e949f74f-0aad-456b-a4ef-6e3d23decfa4","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":103526,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/0ef4ac885f268c98c21e8919.docx"},{"id":95007293,"identity":"56e94461-316e-464c-8313-3d32ba2cb0a3","added_by":"auto","created_at":"2025-11-03 09:39:20","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":122710,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/c2ef509a4914d003e5fecc81.docx"},{"id":95220815,"identity":"fce00d1e-2b2b-4c0c-b664-98785f9edcd3","added_by":"auto","created_at":"2025-11-05 16:14:27","extension":"jpg","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":2070591,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicabstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835974/v1/1520a79de7053767bdf4d263.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssociation of Estimated Pulse Wave Velocity and Metabolic Markers With Cognitive Impairment Risk in Diabetes\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDementia is a progressive neurodegenerative syndrome, most often caused by Alzheimer\u0026rsquo;s disease (AD), which accounts for 60\u0026ndash;70% of cases in adults\u0026thinsp;\u0026ge;\u0026thinsp;65 years.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] By 2050, over 100\u0026nbsp;million people are projected to be affected, posing an immense global healthcare burden. Type 2 diabetes mellitus (T2DM) substantially increases the risk of cognitive impairment, vascular dementia (VaD), and AD.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Compared with non-diabetic individuals, those with T2DM have ~\u0026thinsp;1.5 fold higher risk of AD, more than double the risk of VD, and greater likelihood of progressing from mild cognitive impairment (MCI) to dementia.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Parallels between cerebral insulin resistance in AD and systemic insulin dysregulation in T2DM underpin the \u0026ldquo;type 3 diabetes\u0026rdquo; hypothesis.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eHypertension, common in T2DM, accelerates cognitive decline, partly via arterial stiffness (AS)\u0026mdash;a vascular aging marker linked to insulin resistance and adverse cerebrovascular outcomes.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] The brain\u0026rsquo;s high flow circulation is particularly vulnerable to the hemodynamic effects of aortic stiffening.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Carotid\u0026ndash;femoral pulse wave velocity (cfPWV) is the gold standard for AS assessment but requires specialized equipment, limiting its scalability.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Evidence on AS and dementia risk is inconsistent. Estimated PWV (ePWV), calculated from age and mean blood pressure, is a practical surrogate that predicts cardiovascular outcomes comparably to cfPWV.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Recent studies suggest that increased AS is associated with higher PWV measurements among individuals with prediabetes and diabetes.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eSystemic inflammation is another pathway linking T2DM to cognitive decline. C reactive protein (CRP) correlates with worse cognition in T2DM and MCI.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Obesity amplifies vascular and metabolic stress, [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] while biomarkers such as the triglyceride\u0026ndash;glucose (TyG) index offer low-cost risk profiling.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Yet, the joint influence of ePWV and metabolic\u0026ndash;inflammatory markers (TyG, CRP, BMI, HbA1c) on cognition in T2DM remains poorly understood. We therefore examined associations between ePWV and cognitive performance in diabetic adults, evaluating effect modification by TyG, CRP, BMI, and HbA1c.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design\u003c/h2\u003e\u003cp\u003eThis study used data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort of adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45, recruited in 2011 via multistage stratified sampling.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Five waves (2011\u0026ndash;2020) provided sociodemographic and health data. Study protocols were ethics-approved by Peking University, with all participants providing informed consent.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study population\u003c/h2\u003e\u003cp\u003eFrom 17,708 Wave 1 participants, exclusions included non-diabetics, age\u0026thinsp;\u0026lt;\u0026thinsp;45/missing age, baseline cognitive impairment, and extreme ePWV values, yielding 893 diabetic participants for analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Baseline characteristics of included versus excluded individuals are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\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 characters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;17,705\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExcluded\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;16,814\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;891\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSMD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.0 [51.0, 65.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.0 [51.0, 65.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.0 [54.0, 64.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.003\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.093\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,222 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8,783 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e439 (49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,471 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8,020 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e451 (51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime since diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.0 [1.0, 7.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.0 [1.0, 6.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.0 [2.0, 9.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCity/Town\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,912 (11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,751 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e161 (18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVillage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15,759 (89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15,030 (90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e729 (82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11,755 (66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11,264 (67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e491 (55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,521 (31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,155 (31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e366 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e429 (2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e395 (2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11,426 (65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10,909 (65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e517 (58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEx-smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,417 (8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,308 (8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,862 (27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,597 (27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e265 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.965\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone of these\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11,938 (67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11,338 (67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e600 (67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrink but less than once a month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,384 (8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,316 (8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 (8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrink more than once a month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,383 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,160 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e223 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipid lowering drug\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot take\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16,792 (95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16,035 (95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e757 (85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e913 (5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e779 (5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e134 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnti-hypertension drug\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot take\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13,300 (75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12,774 (76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e526 (59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,405 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,040 (24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e365 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.031\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17,111 (98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16,251 (98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e860 (97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e413 (2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e382 (2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15,372 (88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14,657 (88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e715 (81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,093 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,920 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e173 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCSED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.0 [6.0, 13.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.0 [6.0, 13.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.0 [6.0, 12.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleeping time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.0 [5.0, 8.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.0 [5.0, 8.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.0 [5.0, 8.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.675\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaist circumference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.4 [77.6, 92.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.0 [77.2, 91.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.0 [83.0, 97.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.1 [20.8, 25.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.0 [20.7, 25.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.8 [22.5, 27.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.1 [4.9, 5.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.1 [4.9, 5.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.9 [5.3, 7.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.6 [8.2, 9.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.6 [8.2, 9.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.5 [9.0, 10.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFPG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102.2 [94.5, 112.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101.3 [94.0, 110.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150.8 [135.5, 191.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.5 [76.5, 100.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.4 [76.5, 100.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.5 [76.1, 102.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.064\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2 [0.8, 1.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2 [0.8, 1.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.7 [1.1, 2.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLdl Cholesterol (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.9 [2.4, 3.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9 [2.4, 3.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.0 [2.3, 3.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.757\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Cholesterol (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.9 [4.3, 5.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.9 [4.3, 5.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.2 [4.5, 6.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHdl Cholesterol (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3 [1.0, 1.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3 [1.0, 1.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1 [0.9, 1.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP (mg/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 [0.6, 2.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 [0.5, 2.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5 [0.7, 3.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eePWV (m/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.5 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.5 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.8 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eePWV*BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e222.9 (57.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e221.5 (57.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e246.0 (56.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eePWV*CRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.7 [5.0, 21.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.4 [4.9, 20.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.0 [7.0, 30.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eePWV*TyG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.8 (18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.0 (17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.8 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eePWV*HbA1C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.3 (13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.4 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.1 (19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 ePWV and Calculated variables\u003c/h2\u003e\u003cp\u003eWe derived parameters using established formulas: Mean Arterial Pressure (MBP): MBP\u0026thinsp;=\u0026thinsp;Diastolic BP\u0026thinsp;+\u0026thinsp;0.4 * Pulse Pressure, ePWV: ePWV\u0026thinsp;=\u0026thinsp;9.587 - (0.402 * age) + (0.00456 * age\u0026sup2;) - (0.00002621 * age\u0026sup2; * MBP) + (0.003176 * age * MBP) - (0.01832 * MBP), TyG: TyG\u0026thinsp;=\u0026thinsp;ln[Triglycerides (mg/dL) * Fasting Glucose (mg/dL) / 2], Body Mass Index (BMI):BMI\u0026thinsp;=\u0026thinsp;Weight (kg) / [Height (cm)/100]\u0026sup2;, Interaction Terms: ePWV * BMI, ePWV * CRP (mg/l), ePWV * HbA1c(%), ePWV * TyG.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Assessment of cognition function\u003c/h2\u003e\u003cp\u003eCognition was evaluated in 2013, 2015, 2018, and 2020 using the Chinese version of the Mini-Mental State Examination (MMSE), covering episodic memory and mental intactness. Episodic memory was tested via immediate and 5 minute delayed recall of 10 Chinese words (1 point per correct; range: 0\u0026ndash;20). Mental intactness was assessed with the Telephone Interview of Cognitive Status (TICS) and a visuospatial task. TICS measured temporal orientation (year, month, day, season, weekday) and serial subtraction (7 from 100 up to five times). Visuospatial ability involved reproducing a presented figure (correct\u0026thinsp;=\u0026thinsp;1). Scores for mental intactness ranged from 0 to 11. Global cognition was the sum of both domains (range: 0\u0026ndash;31), higher scores indicating better function. Cognitive impairment (CI) was defined as global cognition\u0026thinsp;\u0026lt;\u0026thinsp;11 or being in the lowest quartile for mental intactness (\u0026lt;\u0026thinsp;2.75) or episodic memory (\u0026lt;\u0026thinsp;5). [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] CI onset was the period between the last normal assessment and the first CI record.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Assessment of covariates\u003c/h2\u003e\u003cp\u003eBaseline CHARLS data included: (1) Demographics\u0026mdash;age, sex, education, residence type, healthcare access; (2) Physical measures\u0026mdash;systolic/diastolic blood pressure (SBP/DBP), height, weight; (3) Lifestyle\u0026mdash;smoking, alcohol use, sleep duration; (4) Health conditions\u0026mdash;diabetes duration, use of lipid-lowering/antihypertensive medications, stroke or heart disease history (doctor-diagnosed); (5) Mental health\u0026mdash;10 item CESD-10 (score 0\u0026ndash;30; higher\u0026thinsp;=\u0026thinsp;worse); (6) Laboratory\u0026mdash;CRP, HbA1c, eGFR, triglycerides (TG), LDL-C, total cholesterol, HDL-C. SBP/DBP were averaged from three Omron HEM 7200 readings. BMI was categorized by Chinese standards: underweight (\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;), normal (18.5\u0026ndash;24), overweight (24\u0026ndash;28), obese (\u0026ge;\u0026thinsp;28).[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] eGFR was calculated via the 2012 CKD-EPI equation for diabetics.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Diabetes definition\u003c/h2\u003e\u003cp\u003eParticipants were considered diabetic if they met any of the following:\u003c/p\u003e\u003cp\u003e(1) Biochemical criteria (2005 ADA): fasting glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL (7 mmol/L), random glucose\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL (11.1 mmol/L), or HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%;[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\u003cp\u003e(2) Self-reported diagnosis by a doctor;\u003c/p\u003e\u003cp\u003e(3) Current use of glucose-lowering treatment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables following a normal distribution are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and non-normal variables as median (interquartile range, IQR). Normality was determined by meeting all criteria: absolute skewness\u0026thinsp;\u0026lt;\u0026thinsp;0.5, kurtosis 2.5\u0026ndash;3.5, and \u0026gt;\u0026thinsp;90% of Q\u0026ndash;Q plot points within confidence bands. Categorical variables are expressed as frequency (percentage). Group comparisons used independent t tests (normal continuous), Wilcoxon rank sum tests (non-normal continuous), or χ\u0026sup2; tests (categorical). Standardized mean differences (|SMD| \u0026gt;0.15) indicated clinically relevant imbalance.\u003c/p\u003e\u003cp\u003eMissing baseline and follow up data were imputed using a three-step approach. Categorical variables with \u0026le;\u0026thinsp;5 missing values underwent mode imputation. Continuous variables with \u0026gt;\u0026thinsp;10 valid observations and \u0026lt;\u0026thinsp;80% missingness were imputed using Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching (five datasets, 10 iterations). Variables with insufficient data were imputed by mean substitution. Missing time series data in Wave 5 were processed using a hybrid approach combining MICE, Kalman filtering, and ensemble reconciliation.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eParticipants were stratified by study inclusion. Generalized variance inflation factors (GVIF) assessed multicollinearity. \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e K means clustering identified participant subgroups; the optimal number of clusters was determined using the elbow method or Silhouette coefficient.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] Two clustering strategies were applied: (1) Basic features (BMI, age, diabetes duration, sleep time, CSED, HbA1c, TyG, eGFR, ePWV, HDL, LDL) and (2) Hybrid features (ePWV*TyG, ePWV*BMI, ePWV*HbA1c, ePWV*CRP).\u003c/p\u003e\u003cp\u003eCox proportional hazards models estimated hazard ratios (HRs) with 95% confidence intervals (CIs): Model 1 unadjusted, Model 2 adjusted for demographic, lifestyle, and comorbidity variables, and Model 3 further adjusted for metabolic/laboratory measures. Kaplan\u0026ndash;Meier analyses with log rank tests compared CVD free survival across ePWV related quartiles and clusters. Restricted cubic spline Cox models (3\u0026ndash;6 knots; lowest Akaike information criterion) tested for non-linear associations.\u003c/p\u003e\u003cp\u003eTime dependent ROC curves evaluated predictive accuracy of PWV and interaction terms for 7 year events; AUCs (95% CIs) were calculated via DeLong\u0026rsquo;s method. Generalized estimating equations (GEE) examined longitudinal associations between ePWV, interaction terms, and cognitive performance (episodic memory, mental intactness, overall score) across four survey waves (2013\u0026ndash;2020).\u003c/p\u003e\u003cp\u003eWe assessed robustness using four sensitivity analyses. (1) E-value quantified the confounding strength needed to nullify associations for continuous and quartile exposures. (2) propensity score matching (PSM) created balanced groups (PP, MAP) via 1:1 nearest-neighbor matching (caliper\u0026thinsp;=\u0026thinsp;0.2 SD logit PS; SMD\u0026thinsp;\u0026lt;\u0026thinsp;0.1), followed by re-estimated Cox models for CI risk. (3) Aalen\u0026rsquo;s additive hazards tested time-varying effects via Supremum, Kolmogorov\u0026ndash;Smirnov, and Cram\u0026eacute;r\u0026ndash;von Mises tests. (4) Two-piecewise Cox identified inflection points for ePWV, ePWV*TyG, and ePWV*HbA1c using segmented regression, estimating HRs and 95% CIs below each cut-point in three adjusted models.\u003c/p\u003e\u003cp\u003eAll analyses were performed using R version 4.4.2. Main R packages involving mice, VIM, car, facroextra, survival, rms, parckwork, timereg, segmented, boot, geepack and broom are used, detailed version information can be found on \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline features\u003c/h2\u003e\u003cp\u003eAmong 17,705 eligible adults, 891 entered longitudinal analyses. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Compared with excluded individuals, included participants were slightly older (median 59 vs. 58 years, SMD 0.05) with longer diabetes duration (4 vs. 3 years, SMD 0.21), more likely urban (18% vs. 10%, SMD 0.22), and better educated (high school/tertiary 45% vs. 33%, SMD 0.25). Smoking prevalence was modestly higher (30% vs. 27%, SMD 0.17), while drinking patterns were similar. Cardiometabolic treatment was more common: lipid-lowering drugs (15% vs. 5%, SMD 0.35) and antihypertensives (41% vs. 24%, SMD 0.37). History of heart disease was more frequent (19% vs. 12%, SMD 0.22); stroke prevalence was low (3% vs. 2%).\u003c/p\u003e\u003cp\u003eAdiposity and metabolic markers were less favorable: greater waist circumference (SMD 0.44), BMI (0.45), HbA1c (1.00), TyG (1.29), fasting plasma glucose (1.42), triglycerides (0.56), total cholesterol (0.30), and lower HDL-C (0.42). ePWV was modestly higher (SMD 0.15), while composite indices showed larger group differences: ePWV*TyG (0.62), ePWV*HbA1c (0.84), ePWV*CRP (0.90).\u003c/p\u003e\u003cp\u003eAfter excluding extreme continuous values, 795 and 871 participants were retained for basic (k\u0026thinsp;=\u0026thinsp;3) and hybrid (k\u0026thinsp;=\u0026thinsp;2) K-means analyses, respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e In basic clustering, median ePWV was highest in Cluster 3 (11.40 m/s), defining a high-stiffness phenotype, and lower in Clusters 1\u0026ndash;2 (~\u0026thinsp;8.8\u0026ndash;9.0 m/s). In hybrid clustering, Cluster 1 consistently displayed higher composite stiffness\u0026ndash;metabolic indices versus Cluster 2, delineating a metabolically adverse, high-stiffness profile.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 COX analysis\u003c/h2\u003e\u003cp\u003eHigher arterial stiffness predicted incident cognitive impairment (CI).\u003cb\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e Per-SD ePWV was associated with CI across models: HR 1.44 (95% CI 1.32\u0026ndash;1.58) unadjusted, 1.27 (1.06\u0026ndash;1.51) Model 2, and 1.32 (1.09\u0026ndash;1.58) fully adjusted; corresponding E-values (2.24, 1.85, 1.96) indicated robustness to unmeasured confounding.\u003c/p\u003e\u003cp\u003eComposite indices showed similar patterns. ePWV*TyG per SD was significant unadjusted (HR 1.33) and after full adjustment (HR 1.29, \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.009\u003c/em\u003e). ePWV*HbA1c remained significant in all models (HR\u0026thinsp;~\u0026thinsp;1.15\u0026ndash;1.27, \u003cem\u003eP\u0026thinsp;\u0026le;\u0026thinsp;0.01\u003c/em\u003e). ePWV*BMI was nonsignificant after adjustment. Quartile analyses supported dose\u0026ndash;response relationships, especially for ePWV and ePWV*TyG. For ePWV, Q4 vs. Q1 yielded HR 2.41 unadjusted (\u003cem\u003eP trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) with attenuated but consistent direction in Model 3.\u003c/p\u003e\u003cp\u003eCluster-based analyses showed that the high-stiffness basic cluster (Cluster 3) had elevated risk versus Cluster 1 in Model 1 (HR 1.38, \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.007\u003c/em\u003e) but lost significance after adjustment. In hybrid clustering, the lower-risk cluster was protective unadjusted (HR 0.67, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) but not in Models 2\u0026ndash;3, indicating model dependent effects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 K-M survival analysis\u003c/h2\u003e\u003cp\u003eSurvival curves revealed graded declines in CI-free survival with higher ePWV indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For ePWV quartiles, 2-year survival decreased from 0.805 (Q1) to 0.656 (Q4). Median survival was 7.0 years for Q3 and 4.0 years for Q4 (log-rank χ\u0026sup2; = 55.5, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). Similar dose\u0026ndash;response trends were seen for ePWV*BMI, ePWV*TyG, ePWV*HbA1c, and ePWV*CRP quartiles.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBasic clustering identified the high-stiffness group (Cluster 3) with the steepest survival decline\u0026mdash;2-, 4-, 7-, and 9-year rates: 0.709, 0.504, 0.369, 0.335\u0026mdash;versus intermediate and low-risk clusters (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). Hybrid clustering separated a metabolically adverse cluster with lower survival from a lower-risk profile (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Restricted cubic spline analysis\u003c/h2\u003e\u003cp\u003eTest results of k-nodes are shown in \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e and \u003cb\u003eTable \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e. RCS models (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e) showed nonlinear relationships. For ePWV, risk crossed unity at 9.53 m/s and reached HR 1.20 at 10.30 m/s; above 7.5 m/s, each 2.5-unit increase raised risk by ~\u0026thinsp;64\u0026ndash;98%. ePWV*TyG had its lowest HR (0.50) at 52.1, unity at 90.41, and HR 1.20 at 101.22; per 25-unit increase, risk rose by 50\u0026ndash;58%. ePWV*HbA1c\u0026rsquo;s lowest HR (0.56) occurred at 31.8, unity at ~\u0026thinsp;60, and HR 1.20 at 92.05.\u003c/p\u003e\u003cp\u003eePWV*BMI exhibited a W-shaped association, with modest HR changes (1.01\u0026ndash;1.68) across plausible ranges. log(ePWV*CRP) showed a U shape, with lowest risk at CRP\u0026thinsp;~\u0026thinsp;2.12 and higher risk at both ends.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.5 ROC analysis\u003c/h2\u003e\u003cp\u003eTime dependent ROC curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) evaluated 7-year CI prediction. ePWV had an AUC of 0.685 (95% CI 0.645\u0026ndash;0.725); the optimal cut off (9.48 m/s) gave 63.3% sensitivity and 66.1% specificity (Youden\u0026thinsp;=\u0026thinsp;0.294). ePWV*TyG yielded AUC 0.653, cut-off 91.54, sensitivity 57.6%, specificity 65.2% (Youden\u0026thinsp;=\u0026thinsp;0.228). ePWV*HbA1c performed lower (AUC 0.607), with cut off 57.33, sensitivity 64%, specificity 53.0% (Youden\u0026thinsp;=\u0026thinsp;0.170).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.6 GEE analysis\u003c/h2\u003e\u003cp\u003eAcross four waves, higher ePWV predicted lower cognitive scores after full adjustment. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Per-SD increases were associated with declines in episodic memory (β = \u0026minus;0.48, \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.002\u003c/em\u003e), mental intactness (β = \u0026minus;0.26, \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.038\u003c/em\u003e), and overall cognition (β = \u0026minus;0.71, \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.005\u003c/em\u003e). Composite measures reinforced these associations. ePWV*TyG per SD was consistently significant across domains (episodic β = \u0026minus;0.39; mental β = \u0026minus;0.27; overall β = \u0026minus;0.64; all \u003cem\u003eP\u0026thinsp;\u0026le;\u0026thinsp;0.031\u003c/em\u003e). ePWVHbA1c showed significance for episodic memory (β = \u0026minus;0.29, \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.011\u003c/em\u003e) and borderline for overall cognition (β = \u0026minus;0.34, \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.059\u003c/em\u003e). Quartile analyses revealed dose\u0026ndash;response relationships in minimally adjusted models (Q4 most negative), with attenuation but consistent directionality in fully adjusted models.\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\u003eGEE Analysis of ePWV and Cognitive Function Variables (Episodic memory, Mental Intactness, and Overall Score) in Relation to Risk in Diabetic Patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"19\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eEpisodic memory score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e\u003cp\u003eMental intactness score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c19\" namest=\"c14\"\u003e\u003cp\u003eOverall score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eβ(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eβ(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eβ(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eβ(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003eβ(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003eβ(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eβ(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eePWV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.86 (-1.02, -0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.39 (-0.68, -0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.009\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.48 (-0.78, -0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.44 (-0.57, -0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.26 (-0.50, -0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003e0.037\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.26 (-0.51, -0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003e0.038\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.19 (-1.44, -0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-0.64 (-1.11, -0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cem\u003e0.008\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e-0.71 (-1.21, -0.22)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cem\u003e0.005\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eePWV*TyG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.71 (-0.88, -0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.23 (-0.46, -0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.046\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.39 (-0.70, -0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e0.016\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.39 (-0.52, -0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.19 (-0.37, -0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003e0.038\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.27 (-0.52, -0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003e0.031\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.01 (-1.26, -0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-0.43 (-0.79, -0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cem\u003e0.017\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e-0.64 (-1.13, -0.14)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cem\u003e0.012\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eePWV*HbA1c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.56 (-0.73, -0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.23 (-0.41, -0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.016\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.29 (-0.52, -0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e0.011\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.31 (-0.44, -0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.11 (-0.26, 0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003e0.144\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.09 (-0.27, 0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003e0.341\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-0.78 (-1.03, -0.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-0.31 (-0.60, -0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cem\u003e0.034\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e-0.34 (-0.69, 0.01)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cem\u003e0.059\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eePWV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.28 (-0.74, 0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e0.228\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13 (-0.33, 0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.583\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09 (-0.37, 0.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e0.701\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.04 (-0.33, 0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e0.825\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.22 (-0.15, 0.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003e0.24\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.22 (-0.16, 0.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003e0.26\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-0.24 (-0.97, 0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cem\u003e0.516\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.26 (-0.46, 0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cem\u003e0.479\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e0.24 (-0.48, 0.96)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cem\u003e0.518\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.15 (-1.65, -0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.27 (-0.80, 0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.328\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.25 (-0.79, 0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e0.358\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.25 (-0.63, 0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e0.194\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.15 (-0.29, 0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003e0.503\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.21 (-0.23, 0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003e0.357\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.38 (-2.16, -0.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-0.26 (-1.12, 0.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cem\u003e0.552\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e-0.18 (-1.04, 0.68)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cem\u003e0.686\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.07 (-2.53, -1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.39 (-1.09, 0.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.267\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.48 (-1.20, 0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e0.191\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.98 (-1.36, -0.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.14 (-0.72, 0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003e0.648\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.09 (-0.69, 0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003e0.771\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.76 (-3.50, -2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-0.52 (-1.65, 0.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cem\u003e0.366\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e-0.52 (-1.69, 0.65)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cem\u003e0.383\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eePWV*TyG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.63 (-1.13, -0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e0.012\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.29 (-0.76, 0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.222\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.33 (-0.79, 0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e0.164\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.19 (-0.56, 0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e0.324\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.11 (-0.48, 0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003e0.556\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.10 (-0.49, 0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003e0.624\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-0.58 (-1.33, 0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cem\u003e0.133\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-0.31 (-1.03, 0.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cem\u003e0.391\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e-0.31 (-1.05, 0.44)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cem\u003e0.418\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.29 (-1.76, -0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.60 (-1.07, -0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.013\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.63 (-1.11, -0.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e0.011\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.38 (-0.75, -0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e0.048\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.21 (-0.59, 0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003e0.298\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.17 (-0.60, 0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003e0.449\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.10 (-1.84, -0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-0.53 (-1.28, 0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cem\u003e0.165\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e-0.52 (-1.35, 0.31)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cem\u003e0.217\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.40 (-1.87, -0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.53 (-1.04, -0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.042\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.62 (-1.19, -0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e0.035\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.99 (-1.36, -0.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.41 (-0.89, 0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003e0.095\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.43 (-1.02, 0.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003e0.148\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.68 (-3.40, -1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-1.06 (-1.99, -0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cem\u003e0.025\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e-1.23 (-2.35, -0.11)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cem\u003e0.032\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eePWV*HbA1c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.47 (-0.95, 0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e0.051\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.21 (-0.66, 0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.361\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.25 (-0.71, 0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e0.283\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.12 (-0.49, 0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e0.547\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.04 (-0.33, 0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003e0.833\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.06 (-0.32, 0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003e0.771\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-0.60 (-1.37, 0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cem\u003e0.13\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-0.18 (-0.91, 0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cem\u003e0.631\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e-0.19 (-0.92, 0.55)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cem\u003e0.614\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.76 (-1.23, -0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.21 (-0.68, 0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.387\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.25 (-0.76, 0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e0.349\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.31 (-0.68, 0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e0.105\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.05 (-0.33, 0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003e0.786\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.11 (-0.28, 0.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003e0.584\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.43 (-2.18, -0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-0.50 (-1.25, 0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cem\u003e0.184\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e-0.47 (-1.24, 0.29)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cem\u003e0.226\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.94 (-2.40, -1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.64 (-1.22, -0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.031\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.84 (-1.53, -0.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e0.017\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.73 (-1.12, -0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.19 (-0.63, 0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003e0.389\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.11 (-0.60, 0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003e0.652\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.85 (-2.61, -1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-0.61 (-1.45, 0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cem\u003e0.15\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e-0.60 (-1.53, 0.34)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cem\u003e0.209\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Sensitivity analysis\u003c/h2\u003e\u003cp\u003eWe performed four sensitivity analyses: E-value, PSM-balanced Cox, Aalen\u0026rsquo;s additive hazards, and two-piecewise Cox regression. After PSM, ePWV, ePWV*TyG, and ePWV*CRP remained significantly associated with CI. \u003cb\u003eTable \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e\u003c/b\u003e Aalen\u0026rsquo;s models showed good fit and stability after full adjustment. \u003cb\u003eTable \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e\u003c/b\u003e Two-piecewise Cox indicated protective effects for low ePWV (\u0026lt;\u0026thinsp;8.48 m/s) and ePWV*HbA1c (\u0026lt;\u0026thinsp;99.03), with ePWV*HbA1c (\u0026lt;\u0026thinsp;119.4) remaining significant after full adjustment (HR\u0026thinsp;=\u0026thinsp;0.591, \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.029\u003c/em\u003e). \u003cb\u003eTable \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e, Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this large, nationally representative cohort of Chinese adults with diabetes, we found that higher ePWV\u0026mdash;an accessible surrogate for arterial stiffness\u0026mdash;was significantly associated with an increased risk of incident CI over a median follow-up of seven years. Associations were robust to extensive adjustment for sociodemographic, lifestyle, and clinical variables, with HR of ~\u0026thinsp;1.3 per SD increase and ~\u0026thinsp;1.2 per 1 m/s increase in ePWV.\u003c/p\u003e\u003cp\u003eComposite indices integrating ePWV with metabolic measures\u0026mdash;particularly the TyG index and HbA1c\u0026mdash;demonstrated comparable or stronger effects. Both quartile-based and RCS analyses supported clear dose\u0026ndash;response relationships, and Kaplan\u0026ndash;Meier curves showed substantially lower event-free survival in participants with higher ePWV or composite scores. Sensitivity analyses, including PSM, yielded similar findings. Subgroup analysis indicated effect modification by education level and BMI category.\u003c/p\u003e\u003cp\u003ePrior studies have identified elevated ePWV as a strong predictor of cardiovascular morbidity, stroke, and all-cause mortality, independent of traditional risk factors.[\u003cspan additionalcitationids=\"CR27 CR28 CR29\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] Effect sizes for composite cardiovascular outcomes are comparable to those of gold standard cfPWV.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Although the link between ePWV and cerebral small vessel disease (CSVD) is less studied, evidence is emerging. Higher ePWV has been associated with silent lacunar infarcts[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and increased white matter hyperintensity (WMH) volume, even after vascular risk adjustment.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] WMHs, in turn, are linked to a two-fold higher dementia risk and three-fold higher stroke risk.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eOur results align with work in general populations showing arterial stiffness as an independent dementia risk factor. For example, one large cohort study found that each 1 m/s rise in ePWV was linked to adjusted HRs of 1.51 for all-cause dementia, 1.58 for AD, and 1.30 for VaD.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] These effect sizes were moderately higher than ours (HR\u0026thinsp;=\u0026thinsp;1.20 per 1 m/s). The study also reported that highest-tertile ePWV (\u0026gt;\u0026thinsp;12.71 m/s) conveyed markedly elevated risk versus the lowest tertile (\u0026le;\u0026thinsp;11.65 m/s), particularly for AD (HR 3.92). [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eOur quartiles differed (\u0026le;\u0026thinsp;8.63, 8.63\u0026ndash;9.54, 9.54\u0026ndash;10.78, \u0026ge; 10.78 m/s). Unadjusted models showed a strong gradient: Q3 and Q4 vs. Q1 corresponded to HR 1.81 and 2.41, respectively, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. After adjustment, HRs attenuated but remained directionally consistent. Notably, while some cardiovascular studies consider ePWV\u0026thinsp;\u0026gt;\u0026thinsp;10 m/s as \u0026ldquo;high risk,\u0026rdquo;[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] our two-piece regression indicated an inflection at ~\u0026thinsp;8.48 m/s, suggesting that harmful cerebral effects may manifest earlier\u0026mdash;a notion consistent with cfPWV evidence where 8.5 m/s was associated with impaired white matter microstructure.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eModeling ePWV continuously showed significant, independent associations with reduced scores in episodic memory (β = \u0026minus;\u0026thinsp;0.48), mental intactness (β = \u0026minus;\u0026thinsp;0.26), and overall cognition (β = \u0026minus;\u0026thinsp;0.71). These findings extend a substantial body of longitudinal and cross-sectional evidence linking greater arterial stiffness to cognitive decline. In the Whitehall II Imaging Sub study conducted in the UK, baseline measures showing higher cfPWV were linked to subsequent deficits in semantic fluency (B = \u0026minus;\u0026thinsp;0.47, 95% CI: \u0026minus;\u0026thinsp;0.76 to \u0026minus;\u0026thinsp;0.18; P\u0026thinsp;\u0026lt;\u0026thinsp;0.007) and verbal learning (B = \u0026minus;\u0026thinsp;0.36, 95% CI: \u0026minus;\u0026thinsp;0.60 to \u0026minus;\u0026thinsp;0.12; P\u0026thinsp;\u0026lt;\u0026thinsp;0.007).[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]The Toledo Study for Healthy Aging in Spain reported comparable trends, noting that greater cfPWV was tied to weaker memory performance and faster declines in both total recall and free recall over the observation period.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] Parallel evidence from cross sectional analyses reinforces these associations. In NHANES, elevated ePWV correlated negatively with executive function; for non-Hispanic Black adults, this was reflected in reduced Digit Symbol Substitution Test scores (β = \u0026minus;\u0026thinsp;3.47, 95% CI: \u0026minus;\u0026thinsp;3.90 to \u0026minus;\u0026thinsp;3.00; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) after controlling for multiple confounders.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] Findings from the Northern Manhattan Study also revealed that individuals with higher ePWV (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 11\u0026thinsp;\u0026plusmn;\u0026thinsp;2 m/s) presented with poorer global cognition at baseline (β = \u0026minus;\u0026thinsp;0.100, 95% CI: \u0026minus;\u0026thinsp;0.120 to \u0026minus;\u0026thinsp;0.080) and experienced more rapid deterioration in processing speed, episodic and semantic memory, and executive function (β = \u0026minus;\u0026thinsp;0.063, 95% CI: \u0026minus;\u0026thinsp;0.082 to \u0026minus;\u0026thinsp;0.045).[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eSeveral vascular\u0026ndash;metabolic pathways may explain our findings. Increased arterial stiffness raises central pulse pressure and accelerates waveform transmission, causing mechanical damage to cerebral microvessels.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] This impairs vasoreactivity and promotes chronic hypoperfusion, preferentially affecting hippocampal and white matter regions\u0026mdash;key substrates for memory and executive function.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] In diabetes, hyperglycemia, insulin resistance, and dyslipidemia accelerate vascular remodeling, endothelial dysfunction, and low-grade inflammation, compounding the deleterious effects of mechanical vascular load.[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eTo our knowledge, this is the first large-scale study to jointly examine four metabolic dimensions in interaction with ePWV\u0026mdash;TyG index, HbA1c, BMI, and CRP\u0026mdash;in relation to cognitive performance. All four metrics emerged as independent predictors of cognitive decline, irrespective of diabetes status. TyG, a validated surrogate for insulin resistance, has been associated with dementia risk in meta-analyses (OR\u0026thinsp;=\u0026thinsp;1.14, 95% CI 1.12\u0026ndash;1.16).[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] HbA1c\u0026ndash;cognition associations remain inconsistent: a pooled analysis of randomized trials found only modest slowing of cognitive decline at levels\u0026thinsp;\u0026gt;\u0026thinsp;7.0%,[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and both the ADVANCE and VADT trials reported no cognitive benefit from intensive glucose lowering (\u0026lt;\u0026thinsp;6.0\u0026ndash;6.5%).[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] Elevated CRP, a marker of chronic inflammation, has been linked to impaired cerebrovascular reactivity and subsequent executive dysfunction in diabetes.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Similarly, obesity exacerbates central insulin resistance, promoting tau pathology, neurofibrillary tangle formation, and neuronal loss. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eFrom a clinical standpoint, the relationship between T2DM and dementia is bidirectional: cognitive impairment increases vulnerability to severe hypoglycemic events, cardiovascular disease, stroke, and premature mortality.[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] Individuals with both conditions are also at greater risk of acute hyperglycemic crises\u0026mdash;such as diabetic ketoacidosis and hyperglycemic hyperosmolar state\u0026mdash;than those without dementia, reflecting the compounded challenges of managing diabetes in the context of declining cognition and reduced self-care capacity.[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] Early identification and intervention during this critical window may help slow the progression of cognitive decline and reduce the future burden of dementia.[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] Given that ePWV can be readily estimated from routinely collected clinical data, it offers a practical, low-cost, and noninvasive approach for identifying high-risk individuals and potentially enabling self-monitoring in at-risk populations. Integrating vascular stiffness assessment into diabetes care could therefore provide a dual benefit: optimizing cardiometabolic health while preserving cognitive function.\u003c/p\u003e\u003cp\u003eOur results have several implications. First, ePWV is easily calculated from age and blood pressure, enabling widespread, low-cost assessment without the need for specialized tonometry or imaging. The identified threshold of approximately 8.5 m/s for 7-year risk discrimination may facilitate earlier identification of individuals warranting closer cognitive monitoring or preventive interventions, though external validation is needed. Second, pairing ePWV with routine metabolic markers such as TyG or HbA1c may enhance early risk detection in diabetes, allowing for finer personalization of vascular\u0026ndash;metabolic risk control. Third, the observed modifying effects of education and BMI suggest that preventive strategies should be tailored, with particular attention to overweight individuals and those with any educational attainment background, who may exhibit heightened vulnerability.\u003c/p\u003e\u003cp\u003eStrengths of this study include the use of a large, nationwide diabetes cohort, prospective design, and the application of multiple complementary statistical approaches\u0026mdash;Cox regression, Kaplan\u0026ndash;Meier survival analysis, RCS, clustering, ROC analysis, and extensive sensitivity testing\u0026mdash;to confirm findings. Nonetheless, several limitations merit consideration. First, while ePWV has been shown to predict cardiovascular disease in healthy populations, its prognostic utility in diabetes remains less certain. In this high-risk group, the relationship between vascular stiffness and adverse outcomes may be reciprocal, potentially diminishing ePWV\u0026rsquo;s ability to act as an independent predictor after accounting for coexisting metabolic and vascular abnormalities. This warrants further validation in longitudinal studies specifically designed for diabetic cohorts. Moreover, as an estimated measure, ePWV may be less precise than direct carotid\u0026ndash;femoral PWV assessment, although its feasibility and applicability in large-scale epidemiological surveys make it a pragmatic alternative.[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] Second, cognitive outcomes were assessed via standard survey instruments rather than detailed neuropsychological batteries, potentially leading to misclassification. Third, as an observational study, causal inference is limited and residual confounding cannot be completely excluded despite robustness checks. Fourth, clustering analyses did not yield robust independent effects after full adjustment, suggesting that more sophisticated machine-learning phenotyping or additional features may be needed to capture clinically meaningful subgroups. Finally, generalizability beyond Chinese adults with diabetes is uncertain and should be examined in diverse populations.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, in a large prospective cohort of adults with diabetes, higher arterial stiffness, as measured by ePWV, and composite indices combining stiffness with metabolic measures were independently associated with greater risk of cognitive impairment. These associations were dose\u0026ndash;responsive, robust across analytic methods, and modified by certain demographic and anthropometric factors. ePWV and its composites may represent practical, scalable tools for early identification of individuals at heightened risk of cognitive decline in the diabetic population, offering opportunities for timely intervention\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6. Acknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the CHARLS research team and all study participants for their contributions. This research was based on data from the CHARLS database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Author contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLingjie Kong, Guangning Zhang, and Lingyuan Wu contributed to the conception and design of the manuscript; Lingjie Kong contributed to the acquisition and analysis of data; Lingjie Kong, Guangning Zhang, Jun Wu, Lingyuan Wu, Xizhen Zhou, and Linfei Wang contributed to the drafting the text. Lingjie Kong, Xizheng Zhou, and Linfei Wang prepared the figures. All authros reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMedical and Health Technology Project of Hangzhou (grant. A20220601),\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZhejiang Provincal Medical and Health Technology project(grant.2021KY885)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9. Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or used during the current study are available from the corresponding author on reasonable request. Related R code is available on first author by request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e10. Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCHARLS was approved by the Institutional Review Board of Peking University\u0026nbsp;in accordance with the Declaration of Helsinki\u0026nbsp;(approval number: IRB00001052-11015 for the household survey and IRB00001052-11014 for blood samples), and all participants provided //written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e11. Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e12. Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHebert LE, Weuve J, Scherr PA, et al. Alzheimer disease in the United States (2010\u0026ndash;2050) estimated using the 2010 census. Neurology. 2013;80:1778\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXue M, Xu W, Ou YN, et al. Diabetes mellitus and risks of cognitive impairment and dementia: A systematic review and meta-analysis of 144 prospective studies. Ageing Res Rev. 2019;55:100944.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeng X, Zhang H, Zhao Z, et al. Type 3 diabetes and metabolic reprogramming of brain neurons: causes and therapeutic strategies. Mol Med. 2025;31:61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHassing LB, Hofer SM, Nilsson SE, et al. Comorbid type 2 diabetes mellitus and hypertension exacerbates cognitive decline: evidence from a longitudinal study. Age Ageing. 2004;33:355\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTian X, Zuo Y, Chen S, et al. Hypertension, Arterial Stiffness, and Diabetes: a Prospective Cohort Study. Hypertension. 2022;79:1487\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoutouyrie P, Chowienczyk P, Humphrey JD, et al. Arterial Stiffness and Cardiovascular Risk in Hypertension. Circ Res. 2021;128:864\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaurent S, Cockcroft J, Van Bortel L, et al. Expert consensus document on arterial stiffness: methodological issues and clinical applications. Eur Heart J. 2006;27:2588\u0026ndash;605.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreve SV, Blicher MK, Kruger R, et al. Estimated carotid-femoral pulse wave velocity has similar predictive value as measured carotid-femoral pulse wave velocity. J Hypertens. 2016;34:1279\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Y, Xu K, Wu S, et al. Value of estimated pulse wave velocity to identify left ventricular hypertrophy prevalence: insights from a general population. BMC Cardiovasc Disord. 2022;22:157.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuhammad IF, Borne Y, Ostling G, et al. Arterial Stiffness and Incidence of Diabetes: A Population-Based Cohort Study. Diabetes Care. 2017;40:1739\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerreira MT, Leite NC, Cardoso CR, et al. Correlates of aortic stiffness progression in patients with type 2 diabetes: importance of glycemic control: the Rio de Janeiro type 2 diabetes cohort study. Diabetes Care. 2015;38:897\u0026ndash;904.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGorska-Ciebiada M, Saryusz-Wolska M, Borkowska A, et al. C-Reactive Protein, Advanced Glycation End Products, and Their Receptor in Type 2 Diabetic, Elderly Patients with Mild Cognitive Impairment. Front Aging Neurosci. 2015;7:209.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChung CC, Pimentel D, Jor'dan AJ, et al. Inflammation-associated declines in cerebral vasoreactivity and cognition in type 2 diabetes. Neurology. 2015;85:450\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSelman A, Burns S, Reddy AP et al. The Role of Obesity and Diabetes in Dementia. Int J Mol Sci 23 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNayak SS, Kuriyakose D, Polisetty LD, et al. Diagnostic and prognostic value of triglyceride glucose index: a comprehensive evaluation of meta-analysis. Cardiovasc Diabetol. 2024;23:310.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao Y, Hu Y, Smith JP, et al. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43:61\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo L, Feng W, Mei M, et al. Greater variability in HDL-C was associated with an increased risk of cognitive decline in the middle- and elderly Chinese: A cohort study. Arch Gerontol Geriatr. 2024;125:105503.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrimmins EM, Kim JK, Langa KM, et al. Assessment of cognition using surveys and neuropsychological assessment: the Health and Retirement Study and the Aging, Demographics, and Memory Study. J Gerontol B Psychol Sci Soc Sci. 2011;66(1):i162\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu H, Zou L, Zhou R, et al. Long-Term Increase in Cholesterol Is Associated With Better Cognitive Function: Evidence From a Longitudinal Study. Front Aging Neurosci. 2021;13:691423.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiao Y, Hong X, Neelagar R, et al. Association between glycated hemoglobin A1c levels, control status, and cognitive function in type 2 diabetes: a prospective cohort study. Sci Rep. 2025;15:5011.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe W, Li Q, Yang M, et al. Lower BMI cutoffs to define overweight and obesity in China. Obes (Silver Spring). 2015;23:684\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnson DW, Jones GR, Mathew TH, et al. Chronic kidney disease and automatic reporting of estimated glomerular filtration rate: new developments and revised recommendations. Med J Aust. 2012;197:224\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRefardt J. Diagnosis and differential diagnosis of diabetes insipidus: Update. Best Pract Res Clin Endocrinol Metab. 2020;34:101398.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAzur MJ, Stuart EA, Frangakis C, et al. Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res. 2011;20:40\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang JZ, Ng MK, Rong H, et al. Automated variable weighting in k-means type clustering. IEEE Trans Pattern Anal Mach Intell. 2005;27:657\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeffernan KS, Jae SY, Loprinzi PD. Association Between Estimated Pulse Wave Velocity and Mortality in U.S. Adults. J Am Coll Cardiol. 2020;75:1862\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVlachopoulos C, Terentes-Printzios D, Laurent S, et al. Association of Estimated Pulse Wave Velocity With Survival: A Secondary Analysis of SPRINT. JAMA Netw Open. 2019;2:e1912831.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJae SY, Heffernan KS, Kurl S, et al. Association between estimated pulse wave velocity and the risk of stroke in middle-aged men. Int J Stroke. 2021;16:551\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen C, Bao W, Chen C, et al. Association between estimated pulse wave velocity and all-cause mortality in patients with coronary artery disease: a cohort study from NHANES 2005\u0026ndash;2008. BMC Cardiovasc Disord. 2023;23:412.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen T, Yin H, Zhou Y et al. Relationship between estimated pulse wave velocity trajectories and cardiovascular disease risk in patients with cardiovascular-kidney-metabolic syndrome stages 0\u0026ndash;3. Nutr Metab Cardiovasc Dis (2025) 104192.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou Y, Zhang Y, Xu G, et al. Association between estimated pulse wave velocity and silent lacunar infarct in a Korean population. Front Cardiovasc Med. 2023;10:1070997.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAriko TA, Aimagambetova B, Gardener H, et al. Estimated Pulse-Wave Velocity and Magnetic Resonance Imaging Markers of Cerebral Small-Vessel Disease in the NOMAS. J Am Heart Assoc. 2024;13:e035691.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWardlaw JM, Valdes Hernandez MC, Munoz-Maniega S. What are white matter hyperintensities made of? Relevance to vascular cognitive impairment. J Am Heart Assoc. 2015;4:001140.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, Ren Y, Wang L, et al. Estimated pulse wave velocity associated with cognitive phenotypes in a rural older population in China: A cohort study. Alzheimers Dement. 2025;21:e14491.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJae SY, Heffernan KS, Kim HJ, et al. Impact of estimated pulse wave velocity and socioeconomic status on the risk of stroke in men: a prospective cohort study. J Hypertens. 2022;40:1165\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHsu PC, Lee WH, Tsai WC, et al. Usefulness of Estimated Pulse Wave Velocity in Prediction of Cardiovascular Mortality in Patients With Acute Myocardial Infarction. Am J Med Sci. 2021;361:479\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBadji A, Noriega A, de la Colina A, Karakuzu, et al. Arterial stiffness cut-off value and white matter integrity in the elderly. Neuroimage Clin. 2020;26:102007.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuri S, Chiesa ST, Zsoldos E, et al. Associations between arterial stiffening and brain structure, perfusion, and cognition in the Whitehall II Imaging Sub-study: A retrospective cohort study. PLoS Med. 2020;17:e1003467.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBareiro FAQ, Carnicero JA, Acha AA, et al. Carotid-femoral pulse wave velocity score, an estimator of cognitive performance in the elderly: results from the Toledo Study for Healthy Aging. Geroscience. 2024;46:5711\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeffernan KS, Stoner L, Meyer ML, et al. Association Between Estimated Pulse Wave Velocity and Cognitive Performance in Older Black and White Adults in NHANES. J Alzheimers Dis. 2022;88:985\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAimagambetova B, Ariko T, Gardener H, et al. Association of estimated pulse wave velocity with cognitive function in a multiethnic diverse population: The Northern Manhattan Study. Alzheimers Dement. 2024;20:4903\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEhtewish H, Arredouani A, El-Agnaf O. Diagnostic, Prognostic, and Mechanistic Biomarkers of Diabetes Mellitus-Associated Cognitive Decline. Int J Mol Sci 23 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang X, Cardoso MA, Yang J, et al. Impact of Intensive Glucose Control on Brain Health: Meta-Analysis of Cumulative Data from 16,584 Patients with Type 2 Diabetes Mellitus. Diabetes Ther. 2021;12:765\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCukierman-Yaffe T, Gerstein HC, Williamson JD, et al. Relationship between baseline glycemic control and cognitive function in individuals with type 2 diabetes and other cardiovascular risk factors: the action to control cardiovascular risk in diabetes-memory in diabetes (ACCORD-MIND) trial. Diabetes Care. 2009;32:221\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLauner LJ, Miller ME, Williamson JD, et al. Effects of intensive glucose lowering on brain structure and function in people with type 2 diabetes (ACCORD MIND): a randomised open-label substudy. Lancet Neurol. 2011;10:969\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmerican Diabetes Association Professional, Practice C. Comprehensive Medical Evaluation and Assessment of Comorbidities: Standards of Care in Diabetes-2025. Diabetes Care. 2025;48:S59\u0026ndash;85. 4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKale M, Wankhede N, Pawar R, et al. AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling. Ageing Res Rev. 2024;101:102497.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreve SV, Laurent S, Olsen MH. Estimated Pulse Wave Velocity Calculated from Age and Mean Arterial Blood Pressure. Pulse (Basel). 2017;4:175\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Estimated pulse wave velocity, Diabetes mellitus, Cognitive impairment, Arterial stiffness, Metabolism","lastPublishedDoi":"10.21203/rs.3.rs-7835974/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7835974/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eType 2 diabetes mellitus (T2DM) is a major modifiable risk factor for dementia, yet cost-effective, scalable methods for early identification of cognitively at-risk individuals remain limited.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the predictive value of estimated pulse wave velocity (ePWV), alone and in combination with metabolic markers, for incident cognitive impairment (CI) in adults with diabetes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing data from 893 participants with T2DM in the China Health and Retirement Longitudinal Study (CHARLS), we prospectively assessed the association of ePWV\u0026mdash;calculated from routine blood pressure and age\u0026mdash;with CI over a 9-year follow-up. Metabolic indicators, including the triglyceride\u0026ndash;glucose (TyG) index and glycated hemoglobin (HbA1c), were integrated into Cox regression and restricted cubic spline analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHigher ePWV was independently associated with an increased risk of CI, even after adjusting for conventional vascular and metabolic covariates. Models combining ePWV with TyG index and HbA1c demonstrated superior predictive discrimination compared with ePWV alone.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eePWV, obtainable from routine clinical measurements, is a practical and scalable tool to identify diabetic individuals at elevated risk for CI. Combining vascular stiffness and metabolic metrics enhances risk stratification, highlighting dual targets for early preventive intervention. These findings support the integration of cardiovascular\u0026ndash;metabolic monitoring into dementia prevention strategies for high-risk populations.\u003c/p\u003e","manuscriptTitle":"Association of Estimated Pulse Wave Velocity and Metabolic Markers With Cognitive Impairment Risk in Diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 09:39:15","doi":"10.21203/rs.3.rs-7835974/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-28T03:53:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-27T10:18:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-16T07:25:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319915672939247920522348022326226993856","date":"2025-11-14T20:58:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164162770904942140983537134764622457109","date":"2025-11-13T09:52:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15165865194601828727004310138983575432","date":"2025-11-13T04:41:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-21T20:08:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-19T23:41:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-19T23:40:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2025-10-11T14:26:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"482e32ec-b5e8-4f62-a1f6-695fd99bd53b","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:15:58+00:00","versionOfRecord":{"articleIdentity":"rs-7835974","link":"https://doi.org/10.1186/s12902-026-02247-5","journal":{"identity":"bmc-endocrine-disorders","isVorOnly":false,"title":"BMC Endocrine Disorders"},"publishedOn":"2026-03-30 15:57:45","publishedOnDateReadable":"March 30th, 2026"},"versionCreatedAt":"2025-11-03 09:39:15","video":"","vorDoi":"10.1186/s12902-026-02247-5","vorDoiUrl":"https://doi.org/10.1186/s12902-026-02247-5","workflowStages":[]},"version":"v1","identity":"rs-7835974","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7835974","identity":"rs-7835974","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}