Joint associations of the atherogenic index of plasma and surrogate indices of insulin resistance with incident cardiovascular disease in middle-aged and older Chinese adults

Joint associations of the atherogenic index of plasma and surrogate indices of insulin resistance with incident cardiovascular disease in middle-aged and older Chinese adults | 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 Joint associations of the atherogenic index of plasma and surrogate indices of insulin resistance with incident cardiovascular disease in middle-aged and older Chinese adults Kun Xue, Haibin Dong, Yiming Wang, Kaixuan Fu, Bowen Xu, Jikai Song, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9361985/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The triglyceride-glucose (TyG) index, estimated glucose disposal rate (eGDR), and metabolic score for insulin resistance (METS-IR) are established surrogate markers of insulin resistance (IR), while the atherogenic index of plasma (AIP) reflects atherogenic dyslipidemia. Nevertheless, evidence regarding their combined value in cardiovascular disease (CVD) risk stratification remains limited. We therefore examined the joint associations of AIP and three IR surrogate indices with incident CVD. Methods: In this prospective research, 4,117 adults aged 45 years or more from the China Health and Retirement Longitudinal Study (CHARLS) were included, all of whom were free of cardiovascular disease at the beginning. The median was used to split AIP and each IR surrogate index into two categories. Cox models, restricted cubic spline analyses, 7-year time-dependent ROC analyses, integrated discrimination improvement, net reclassification improvement, mediation analyses, subgroup analyses, and sensitivity analyses were conducted. Results: During follow-up, 749 participants developed incident CVD. In fully adjusted models, the highest combined categories of TyG-AIP, eGDR-AIP, and METS-IR-AIP were associated with higher CVD risk:1.21, 95% CI: 1.03, 1.43; 1.82, 95% CI: 1.39, 2.37 and 1.43, 95% CI:1.19, 1.73. Among participants with high AIP, only eGDR showed a significant nonlinear association with incident CVD. AIP alone provided limited improvement in discrimination, whereas eGDR showed the greatest predictive gain, increasing the 7-year AUC from 0.578 to 0.628; METS-IR showed a smaller improvement, while TyG added little. Reclassification analyses showed a similar pattern. Mediation analyses indicated that the association between AIP and incident CVD was partly mediated by eGDR and, to a lesser extent, by METS-IR, whereas no significant mediation was observed for TyG. No significant multiplicative or additive interaction was observed between AIP and TyG or eGDR, whereas AIP and METS-IR showed a significant antagonistic interaction on the multiplicative scale. Conclusion Higher AIP and adverse IR profiles were jointly associated with incident CVD. Among the three surrogate indices, eGDR showed the most informative overall profile. Combined assessment of AIP and eGDR may improve cardiovascular risk stratification. Cardiac & Cardiovascular Systems Insulin resistance Atherogenic index of plasma Cardiovascular disease CHARLS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Despite improvements in medical and public health methods, cardiovascular disease (CVD) has been the primary cause of mortality and disability worldwide for decades[ 1 ]. Global Burden of Disease (GBD) research has continuously pointed out that CVD has a great impact on global health, among which ischemic heart disease and stroke are the main causes of this problem[ 2 , 3 ]. From 1990 to now, the number of people suffering from CVD has more than doubled, and the death toll has increased from 13.1 million in 1990 to 19.2 million in 2023[ 2 ]. To reduce the global CVD burden, we must focus on controlling these risk factors and adopt targeted public health strategies and actions. Insulin resistance(IR) has been increasingly recognized as an important contributor to CVD[ 4 ], which is mainly related to vascular endothelial metabolism [ 5 ], inflammatory[ 6 ], and dysfunction[ 7 ]. In extensive epidemiological research, various surrogate markers such as the triglyceride-glucose index (TyG), metabolic score for insulin resistance (METS-IR), and estimated glucose disposal rate (eGDR) are commonly utilized to assess CVD risk associated with IR[ 8 , 9 ]. At the same time, the atherogenic index of plasma (AIP), an indicator of atherogenic dyslipidemia, has also been linked to cardiovascular outcomes [ 10 , 11 ]. It is worth noting that there may be close interaction between AIP and IR. A cross-sectional study found that AIP showed an inverse L-shaped association with IR[ 12 ]. However, one study found that in the presence of IR, an elevated TG/HDL-C ratio was as effective as the commonly used total cholesterol/HDL-C ratio in predicting CHD risk[ 13 ]. In addition, AIP has been associated with diabetes[ 14 , 15 ] and metabolic syndrome[ 16 ], suggesting its potential value in predicting diabetic complications and informing early intervention strategies. Considering the close biological relationship between IR and atherogenic dyslipidemia, a better understanding of their joint influence on the risk of CVD may help to find the risk earlier. However, prospective evidence on whether AIP provides complementary information beyond established IR surrogate indices for incident CVD remains limited. Therefore, we used data from the China Health and Retirement Longitudinal Study (CHARLS) to investigate the associations of AIP and three surrogate indices of IR with incident CVD, as well as their joint associations. We further compared their incremental predictive value and potential mediating pathways. Methods Study design and participants CHARLS used a multistage probability sampling design to achieve a sample that represents the nation. A total of 17,708 adults aged 45 and above were enrolled in the baseline survey, spanning 450 communities within 150 counties or districts across 28 provinces in China. The study obtained permission from the Peking University Ethics Review Committee (IRB00001052-11015), and all subjects provided written informed consent. A detailed description of the CHARLS data collection process is shown in Fig. 1 . In order to ensure the integrity of the data, we excluded the participants who met the following conditions: (1) those who lacked the data of TyG, eGDR, METS-IR and AIP; (2) People who have a history of CVD in Wave1 stage or who have lost their follow-up; (3) Participants under the age of 45; (4) participants with less than 2 years follow-up; (5) People who lack CVD diagnosis data in 2013, 2015 or 2018. Data collection In the CHARLS survey, the staff collected demographic characteristics, lifestyle, medical history, anthropometric data and laboratory indicators according to standard procedures after training. Through the structured questionnaire, details about age, gender, marital status, educational background, smoking and drinking habits, along with medical conditions like hypertension, diabetes, heart disease, and stroke diagnosed by physicians were collected. Weight and waist circumference (WC) are measured directly, while blood pressure is taken as the average of three consecutive readings. Qualified medical staff collected fasting venous blood samples and processed them according to the CHARLS protocol. After storage and cold chain transportation, the samples were sent to the central laboratory for biochemical analysis. They measured serum total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and fasting blood glucose (FPG) by enzymatic method, while high-sensitivity C-reactive protein (hs-CRP) was measured by immunoturbidimetry. Calculation of insulin resistance surrogate indices and AIP We employ three established surrogate indicators to assess insulin resistance, derived from standard biochemical testing and anthropometric measurements[ 17 ]. The TyG index calculation is ln [TG (mg/dL) × FPG (mg/dL)/2]. The formula for computation is 21.158 − 0.09 x WC (cm) − 3.407 x hypertension (yes = 1, no = 0) − 0.551 x HbA1c (%). METS-IR is determined by the formula ln[(2 x FPG (mg/dL)) + TG (mg/dL)] x BMI (kg/m²) / ln [HDL-C (mg/dL)]. The AIP calculation method is log10 (TG/HDL-C), with TG and HDL-C represented in molar concentrations. Outcome definition and ascertainment The primary outcome was incident cardiovascular disease (CVD), which was ascertained during the second to fourth follow-up surveys. According to previous CHARLS-based research[ 18 ], CVD was characterized as self-reported physician-diagnosed heart disease or stroke, or the utilization of prescription medication for these ailments. Participants were categorized as having incident cardiovascular disease if any of these events were initially reported during the follow-up period. The follow-up duration was determined from baseline to the initial report of a CVD incident. Covariates Demographic traits, lifestyle habits, clinical data, existing health conditions, and medication usage were used to select baseline covariates. Covariates included Covariates encompassed age, sex, smoking status, alcohol consumption, educational attainment, marital status, systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), high-sensitivity C-reactive protein (hs-CRP), uric acid (UA), Blood Urea Nitrogen (BUN), serum creatinine (Scr), hypertension, diabetes, and dyslipidemia. Hypertension was defined as a self-reported physician diagnosis, an SBP of 140 mmHg or above, a DBP of 90 mmHg or higher, or the use of antihypertensive medication[ 19 ]. Diabetes was defined as self-reported physician diagnosis, fasting blood glucose > = 126 mg/dL, HbA1c > = 6.5%, or use of antidiabetic medication[ 20 ]. Dyslipidemia was defined as self-reported physician diagnosis, use of lipid-lowering medication, or abnormal lipid levels, including TG > = 150 mg/dL, LDL-C > = 160 mg/dL, TC > = 240 mg/dL, or HDL-C < 40 mg/dL [ 21 ]. Handling of missing data Several variables had missing values, as shown in Supplementary Fig. S1 . To reduce the impact of incomplete data, we used Multiple Imputation by Chained Equations to generate five imputed datasets, and estimates were pooled according to Rubin's rules. Statistical analysis Depending on their distribution, continuous variables are expressed as mean ± standard deviation (SD) or median (interquartile range, IQR), and comparisons were made using the Student's t test or Mann-Whitney U test. Continuous variables are expressed as mean ± SD for comparability and categorical variables are given as numbers (percentages) and were compared using either the chi-square test or Fisher's exact test. Because no established clinical cut-off values are available for TyG, eGDR, METS-IR, or AIP, these indices were dichotomized at the median. For joint association analyses, participants were classified into four groups according to each IR surrogate index and AIP: low IR/low AIP, low IR/high AIP, high IR/low AIP, and high IR/high AIP. Because lower eGDR values indicate greater insulin resistance, participants with eGDR below the median were categorized as having high IR. Kaplan-Meier curves and the log-rank test were used to compare cumulative CVD incident among groups, with incidence rates estimated per 1,000 person-years. Cox proportional hazards models were employed to assess the associations between the aggregated AIP categories and each IR surrogate index for incident CVD, with outcomes reported as hazard ratios (HRs) and 95% confidence intervals (CIs). Schoenfeld residuals were employed to assess the proportional hazards assumption. Multicollinearity diagnostics ( Tables S1–S3 ) indicated that all generalized variance inflation factors (GVIFs) were below 5, signifying the absence of significant multicollinearity across the covariates[ 22 ]. We established three models: Model 1 was unadjusted. Model 2 included adjustments for age, sex, smoking and drinking status, education level, and marital status. Model 3 incorporated additional adjustments for hs-CRP, UA, Scr, and BUN. Further analyses using restricted cubic splines (RCS) were conducted to explore potential nonlinear relationships between each IR surrogate index and the occurrence of CVD based on AIP strata. To see if the prediction is accurate, we use the 7-year time-dependent ROC curve to further examine the Cox model, and then use an area called time-dependent AUC to measure its distinguishing ability. We also use NRI and IDI methods to see if the new model predicts better than the basic model[ 23 ]. Mediation analyses were performed using the mediation package and accelerated failure time models to explore potential bidirectional mediation patterns between AIP and IR surrogate indices in relation to time to incident CVD. Total, direct, and indirect effects were estimated, and the proportion mediated was calculated [ 24 ]. Subgroup analyses were performed to examine whether the associations between the combined categories of AIP and IR surrogate indices and incident CVD were consistent across strata of age, sex, BMI, smoking, drinking, hypertension, diabetes, and dyslipidemia. Effect modification was assessed by including interaction terms. Additive interaction was evaluated using the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (SI) with the interactionR package. Several sensitivity analyses were also made. We further repeated the joint association analyses in participants with complete data only. Index-specific sensitivity analyses were further conducted by additionally adjusting for selected clinical conditions according to the surrogate index evaluated, while avoiding adjustment for variables overlapping with the construction of that index. In addition, heart disease and stroke were analyzed separately as supplementary outcomes to assess whether the associations differed across specific cardiovascular endpoints. All the analysis was done with R software (version 4.4.3). Mediation package is used for mediation analysis, mice is used to deal with missing data, and survival package is used for Cox regression. P value less than 0.05 (two-sided test) means that the results are statistically significant. Results Baseline characteristics of study participants Table 1 indicates that 749 of the 4,117 participants got new instances of cardiovascular disease during the follow-up period. In comparison to people without CVD, those with newly diagnosed CVD were older, predominantly female, and had a higher prevalence of hypertension, diabetes, and dyslipidemia. They also differed with respect to marital status and smoking status, and had higher BMI, blood pressure, WC, TC, LDL-C, FPG, and HbA1c levels. AIP, TyG, and METS-IR were higher, whereas HDL-C and eGDR were lower. No significant differences were observed for education level, drinking status, TG, Scr, UA, or hs-CRP; BUN was slightly lower in the CVD group. Table 1 Baseline characteristics of the participants classified by CVD Variable Overall Non-CVD CVD p n 4117 3368 749 Age, years 58.08 (8.72) 57.72 (8.74) 59.72 (8.47) < 0.001 Sex, n (%) Female 2247 (54.6) 1791 (53.2) 456 (60.9) < 0.001 Male 1870 (45.4) 1577 (46.8) 293 (39.1) Education level, n (%) Senior high school and above 448 (10.9) 362 (10.7) 86 (11.5) 0.604 Junior high school and below 3669 (89.1) 3006 (89.3) 663 (88.5) Marriage group, n (%) Married 3717 (90.3) 3060 (90.9) 657 (87.7) 0.011 Other 400 (9.7) 308 (9.1) 92 (12.3) Smoking, n (%) Never 2541 (61.7) 2061 (61.2) 480 (64.1) 0.022 Former 304 (7.4) 238 (7.1) 66 (8.8) Current 1272 (30.9) 1069 (31.7) 203 (27.1) Drinking, n (%) No 2539 (61.7) 2067 (61.4) 472 (63.0) 0.426 Yes 1578 (38.3) 1301 (38.6) 277 (37.0) Hypertension, n (%) No 2542 (61.7) 2185 (64.9) 357 (47.7) < 0.001 Yes 1575 (38.3) 1183 (35.1) 392 (52.3) Diabetes, n (%) No 3500 (85.0) 2890 (85.8) 610 (81.4) 0.003 Yes 617 (15.0) 478 (14.2) 139 (18.6) Dyslipidemia, n (%) No 2330 (56.6) 1935 (57.5) 395 (52.7) 0.021 Yes 1787 (43.4) 1433 (42.5) 354 (47.3) BMI (kg/m 2 ) 23.68 (3.91) 23.50 (3.83) 24.47 (4.19) < 0.001 SBP (mmHg) 129.56 (20.64) 128.62 (20.18) 133.80 (22.08) < 0.001 DBP (mmHg) 75.37 (11.68) 74.99 (11.52) 77.08 (12.21) < 0.001 WC (cm) 84.29 (12.92) 83.68 (12.68) 87.02 (13.62) < 0.001 TC (mg/dl) 194.48 (38.81) 193.86 (39.02) 197.25 (37.75) 0.031 TG (mg/dl) 131.90 (109.20) 130.47 (110.11) 138.29 (104.85) 0.076 HDL (mg/dl) 51.32 (15.27) 51.63 (15.33) 49.91 (14.89) 0.005 LDL (mg/dl) 118.05 (35.11) 117.50 (34.80) 120.54 (36.39) 0.032 FPG (mg/dl) 109.97 (33.64) 109.09 (31.92) 113.96 (40.24) < 0.001 HbA1C (%) 5.27 (0.80) 5.24 (0.76) 5.38 (0.95) < 0.001 BUN (mg/dL) 15.60 (4.25) 15.68 (4.31) 15.24 (3.97) 0.010 Scr (mg/dL) 0.77 (0.18) 0.77 (0.19) 0.76 (0.18) 0.482 UA (mg/dl) 4.35 (1.21) 4.36 (1.21) 4.31 (1.22) 0.335 hs-CRP (mg/L) 2.41 (6.06) 2.34 (5.66) 2.74 (7.59) 0.102 AIP 0.35 (0.34) 0.34 (0.34) 0.39 (0.33) < 0.001 TyG 8.68 (0.66) 8.66 (0.65) 8.77 (0.67) < 0.001 eGDR 9.37 (2.32) 9.54 (2.26) 8.58 (2.41) < 0.001 METS-IR 35.88 (8.63) 35.49 (8.42) 37.64 (9.30) < 0.001 Data are presented as mean ± SD or n (%). Abbreviations: CVD, cardiovascular disease; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; AIP, atherogenic index of plasma; TyG, triglyceride-glucose; eGDR, estimate glucose disposal rate; METS-IR, metabolic score for insulin resistance; UA, uric acid; Scr, serum creatinine, hs-CRP, high-sensitivity C-reactive protein; BUN, Blood Urea Nitrogen. Baseline characteristics were compared between groups using χ² or analysis of variance or Kruskal–Wallis rank sum test where appropriate. Association of TyG, eGDR, METS-IR, and AIP with the risk of incident CVD Table S5 indicates that elevated AIP, TyG, and METS-IR levels were strongly correlated with an increased risk of CVD following comprehensive adjustment, while greater eGDR was significantly linked to a reduced risk. According to Table 2 , the relationships between the joint categories of IR surrogate indicators and AIP and the incidence of CVD varied. After full adjustment, participants in the highest TyG-AIP category had a 21% higher risk of incident CVD than those in the reference group (HR 1.21, 95% CI 1.03–1.43). For the eGDR-AIP combination, excess risk became evident in the less favorable categories and was most pronounced in Group 4, where the risk of CVD was 82% higher than that in the reference group (HR 1.82, 95% CI 1.39–2.37). For METS-IR-AIP, risk was elevated across Groups 2 to 4, with the highest adjusted HR observed in Group 3 (HR 1.58, 95% CI 1.25-2.00), while Group 4 also remained significantly associated with incident CVD (HR 1.43, 95% CI 1.19–1.73). In addition, all three joint indicators showed significant trends across the combined categories. Figure 2 illustrates clear separation in cumulative CVD incidence across the combined TyG-AIP, eGDR-AIP, and METS-IR-AIP groups, with the greatest divergence observed for eGDR-AIP and METS-IR-AIP (all log-rank P < 0.001). Table 2 Combined effects of IR surrogate indices and AIP on CVD risk Variable Model I Model II Model III HR 95% CI p-value HR 95% CI p-value HR 95% CI p-value TyG and AIP Group 1 Ref — Ref — Ref — Group 2 1.30 0.97, 1.75 0.081 1.37 1.02, 1.85 0.038 1.31 0.97, 1.77 0.079 Group 3 1.15 0.85, 1.57 0.362 1.10 0.81, 1.50 0.549 1.35 1.11, 1.63 0.003 Group 4 1.39 1.19, 1.63 < 0.001 1.34 1.15, 1.57 < 0.001 1.21 1.03, 1.43 0.024 P for trend < 0.001 < 0.001 0.019 eGDR and AIP Group 1 Ref — Ref — Ref — Group 2 1.24 0.97, 1.59 0.080 1.23 0.96, 1.57 0.098 1.22 0.95, 1.56 0.119 Group 3 2.03 1.63, 2.53 < 0.001 1.88 1.51, 2.35 < 0.001 1.59 1.20, 2.11 0.001 Group 4 2.32 1.91, 2.83 < 0.001 2.16 1.77, 2.63 < 0.001 1.82 1.39, 2.37 < 0.001 P for trend < 0.001 < 0.001 < 0.001 METS-IR and AIP Group 1 Ref — Ref — Ref — Group 2 1.42 1.12, 1.81 0.004 1.35 1.06, 1.72 0.016 1.31 1.03, 1.67 0.031 Group 3 1.68 1.34, 2.12 < 0.001 1.74 1.39, 2.20 < 0.001 1.58 1.25, 2.00 < 0.001 Group 4 1.63 1.37, 1.94 < 0.001 1.64 1.38, 1.95 < 0.001 1.43 1.19, 1.73 < 0.001 P for trend < 0.001 < 0.001 < 0.001 Model I: non-adjusted Model II: adjusted for age, sex, smoking status, drinking status, education level, marital status Model III: further adjusted for hs-CRP, UA, Scr, BUN. Identifying nonlinear connections To identify possible nonlinear connections, we used multivariable-adjusted restricted cubic spline analyses to study how IR surrogate indices relate to new cases of CVD, divided by AIP level. In the low AIP stratum, significant overall associations were observed for all three indices, but there was no evidence of nonlinearity. There was a positive correlation between TyG and CVD risk, an inverse linear correlation with eGDR, and a positive linear correlation with METS-IR (all P values for overall 0.05; Fig. 3 A-C). In people with elevated AIP, the relationships between TyG and METS-IR and the occurrence of CVD remained significant overall, although neither showed a statistically significant nonlinear component. In contrast, eGDR demonstrated a significant nonlinear association with incident CVD in the high AIP subgroup (Fig. 3 D-F). Further two-piecewise Cox regression analysis identified a significant inflection point for eGDR at 9.26 in the fully adjusted model (P for log-likelihood ratio test < 0.001; Table S4 ). Predictive performance of IR surrogates and AIP for CVD As presented in Fig. 4 , the basic model demonstrated limited predictive performance for 7-year incident CVD, with a time-dependent AUC of 0.578 (95% CI, 0.547–0.609). The addition of AIP was associated with only a slight improvement in discrimination, increasing the AUC to 0.589 (95% CI, 0.559–0.618). When IR surrogate indices were further incorporated, the magnitude of improvement differed across indices. The gain was most pronounced for eGDR, with the AUC increasing to 0.628 (95% CI, 0.598–0.658), followed by METS-IR, with an AUC of 0.611 (95% CI, 0.581–0.641). In contrast, TyG produced only a marginal increase in AUC to 0.594 (95% CI, 0.564–0.624). These patterns were broadly consistent with the reclassification results shown in Table 3 . Specifically, adding eGDR significantly improved reclassification (NRI 0.320, 95% CI 0.123 to 0.404; P = 0.016) and discrimination (IDI 0.019, 95% CI 0.013 to 0.027; P < 0.001), whereas METS-IR showed a smaller but still significant improvement (NRI 0.178, 95% CI 0.088 to 0.311; P = 0.008; IDI 0.010, 95% CI 0.002 to 0.023; P = 0.012). TyG did not significantly improve NRI or IDI. Table 3 Incremental reclassification performance of AIP and IR indices for incident CVD Comparison NRI (95% CI) P value IDI (95% CI) P value Basic + TyG vs Basic 0.179 (-0.010 to 0.228) 0.144 0.004 (-0.004 to 0.017) 0.359 Basic+eGDR vs Basic 0.320 (0.123 to 0.404) 0.016 0.019 (0.013 to 0.027) < 0.001 Basic+METS-IR vs Basic 0.178 (0.088 to 0.311) 0.008 0.010 (0.002 to 0.023) 0.012 Basic + AIP+TyG vs Basic + AIP 0.053 (-0.018 to 0.076) 0.164 0.001 (-0.012 to 0.027) 0.922 Basic + AIP+eGDR vs Basic + AIP 0.310 (0.044 to 0.431) 0.028 0.016 (0.011 to 0.023) < 0.001 Basic + AIP+METS-IR vs Basic + AIP 0.233 (0.022 to 0.294) 0.016 0.007 (0.001 to 0.018) 0.032 The basic model was adjusted for age, sex, smoking status, drinking status, education level, marital status, BUN, Scr, UA, hs-CRP Mediation analyses of IR surrogates and AIP on CVD risk As shown in Fig. 5 , exploratory mediation analyses based on accelerated failure time models suggested that only eGDR and METS-IR showed evidence of mediation in the association between AIP and time to incident CVD. The mediating effect was much stronger for eGDR, which accounted for 49.62% of the association, whereas METS-IR accounted for only 2.69%. No significant mediation was observed for TyG. In the reverse direction, no significant mediation through AIP was detected for any of the three IR surrogate indices. Subgroup and sensitivity analyses Subgroup analyses showed that the associations of the combined TyG-AIP, eGDR-AIP, and METS-IR-AIP categories with incident CVD were broadly consistent across strata of age, sex, smoking status, drinking status, BMI, hypertension, dyslipidemia, diabetes (Fig. 6 and Fig.S2 ). In general, participants in the high-risk combined category had a higher risk of CVD than those in the reference category across most subgroups. After completely adjusting for covariates, no notable interactions were found concerning age, sex, smoking habits, drinking habits, BMI, hypertension, dyslipidemia, or diabetes (P for interaction > 0.05). To confirm the reliability of the main results, multiple sensitivity analyses were conducted. First, all analyses were repeated in complete-case participants (n = 4,103). Second, index-specific sensitivity analyses were further conducted by additionally adjusting for selected clinical conditions according to the surrogate index evaluated, while avoiding adjustment for variables overlapping with the construction of that index. Third, heart disease and stroke were analyzed separately as supplementary outcomes (n = 4,117). Significantly, these analyses produced results that aligned with the primary findings ( Table S6-S9 ). Exploratory interaction analyses are presented in Supplementary Table S10 . No clear multiplicative or additive interaction was observed for AIP with TyG or eGDR, whereas AIP and METS-IR showed an antagonistic interaction on the multiplicative scale. Discussion In this national cohort of 4,117 middle-aged and older Chinese adults, higher AIP combined with a more adverse IR profile was associated with a greater risk of incident CVD, although the strength of this pattern differed across surrogate indices. The joint association was strongest for eGDR and AIP. AIP alone provided limited incremental predictive value, whereas eGDR substantially improved discrimination and reclassification; TyG contributed comparatively little. We also found that the association between AIP and incident CVD may be partly mediated by IR, particularly the component reflected by eGDR, whereas evidence for the reverse pathway was limited. Overall, these findings suggest that combined assessment of AIP and selected IR surrogate indices may improve identification of individuals at high risk of CVD, and that this pattern is robust across subgroup and sensitivity analyses. Our findings are broadly in line with previous studies showing that surrogate markers of IR are associated with cardiovascular risk and that AIP is likewise linked to adverse cardiovascular outcomes. Earlier studies have reported significant associations of TyG, eGDR, and METS-IR with CVD [ 17 , 25 – 27 ], while accumulating evidence has also identified AIP as a marker of atherogenic dyslipidemia and cardiovascular risk[ 28 – 30 ]. First, rather than evaluating these markers in isolation, we examined their joint associations and showed that the coexistence of an adverse IR profile and elevated AIP identified individuals at particularly high risk of incident CVD. Second, by comparing three commonly used IR surrogates within the same prospective cohort, we found that the eGDR-AIP combination showed the strongest association, whereas the corresponding patterns for METS-IR-AIP and especially TyG-AIP were less pronounced. These findings add to the limited prospective evidence on the joint assessment of AIP and established IR surrogates for incident CVD and suggest that the value of combining AIP with IR-related markers may vary according to the surrogate index used. Several mechanisms may help explain the observed joint association of AIP and IR with incident CVD, as well as the stronger performance of eGDR in the present study. One critical pathway involves the promotion of atherosclerosis through reduced vascular smooth muscle cell survival and enhanced inflammatory signaling, such as activation of the CX3CL1/CX3CR1 axis, which may increase plaque vulnerability[ 31 ]. IR is associated with an increase in inflammation, atherosclerosis, oxidative stress, and endothelial dysfunction, factors that lead to the development of CVD[ 32 , 33 ], particularly in younger individuals, indicating that IR not only acts independently but may also amplify other lipid-related risks[ 34 ], particularly in younger individuals, indicating that IR not only acts independently but also amplifies other lipid-related risks[ 35 ]. In addition, AIP has been reported to correlate with increased levels of glucose, insulin, and inflammatory markers such as C-reactive protein[ 36 , 37 ]. When these abnormalities coexist, they may identify a more adverse cardiometabolic milieu than either marker alone. In our study, this pattern was most evident for eGDR, which may be because eGDR incorporates WC, hypertension, and HbA1c, and may therefore better capture the chronic metabolic burden relevant to cardiovascular damage than TyG or METS-IR. In summary, combined assessment of AIP and IR surrogate indices, especially eGDR, may improve early identification of middle-aged and older adults at elevated risk of incident CVD. Strengths and limitations This study has several strengths, including its prospective design, the nationally representative CHARLS cohort, and the simultaneous comparison of three IR surrogate indices in combination with AIP. In addition, we used a comprehensive analytic strategy that included joint association analyses, nonlinear modeling, prediction analyses, mediation analyses, and multiple sensitivity analyses, which strengthened the overall interpretation of the findings. However, several limitations should be considered. First, because of the observational design, causal inferences cannot be made. Second, incident CVD identification relied on self-reported physician diagnoses or medication usage, which could lead to misclassification. Third, TyG, eGDR, and METS-IR are surrogate rather than gold-standard measures of IR, and all exposures were measured at baseline only. Fourth, despite adjustment for a wide range of potential confounders in the multivariable and subgroup analyses, the influence of residual confounding from unmeasured factors, including diet, physical activity, and socioeconomic status, cannot be completely ruled out. Fifth, the joint analyses relied on dichotomization at the median, which facilitated risk stratification but may also have led to some loss of information. Although restricted cubic spline analyses were performed as complementary analyses, the categorization-based results should still be interpreted with this limitation in mind. Finally, because our study was limited to middle-aged and older Chinese adults, additional studies are warranted to evaluate whether these findings can be generalized to other ethnic groups, age ranges, and populations with different lifestyle characteristics. Conclusion In summary, higher AIP and poor IR profiles were linked to incident CVD in this national cohort. Among the three indices, eGDR showed the strongest association with AIP, offering the best predictive value and mediation signal. These results suggest that combining AIP with specific IR markers, particularly eGDR, could enhance cardiovascular risk assessment in middle-aged and older adults. Abbreviations TyG, triglyceride-glucose eGDR, estimate glucose disposal rate METS-IR, metabolic score for insulin resistance AIP, atherogenic index of plasma HDL-C, High-density lipoprotein cholesterol LDL-C, Low-density lipoprotein cholesterol CVD, Cardiovascular disease TG, Triglyceride TC, Total cholesterol hs-CRP, High-sensitivity C-reactive protein HbA1c, glycated hemoglobin IR, Insulin resistance BUN, Blood Urea Nitrogen SBP, systolic blood pressure DBP, diastolic blood pressure BMI, Body mass index FPG, fasting plasma glucose Scr, serum creatinine UA, uric acid Declarations Author contributions KX conceptualized and designed the study. KX, JS, HD, YW, YR, CW, and LG carried out research. XK, KF and BX performed the data analysis. XK composed the initial draft, while LZ and TD aided in data evaluation and revisions. All authors endorsed the final manuscript. Acknowledgements For this investigation, the researchers drew upon the CHARLS database. They extend their sincere appreciation to the CHARLS research team and acknowledge the indispensable role played by every individual who contributed to this study. Funding Funding for the research detailed in this article was provided by the Shandong Province Traditional Chinese Medicine Science and Technology Project, with Grant Numbers M20250608. Data availability The CHARLS website (http://charls.pku.edu.cn/) provides access to the data that underpins the findings of this study. Declarations Ethics approval and consent to participate The Biomedical Ethics Review Committee of Peking University conducted and approved this study, adhering to the Declaration of Helsinki principles. The survey received the nod with approval number IRB00001052-11015, and the blood collection process was approved under IRB00001052-11014. Each participant penned their informed consent in writing. 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Mahdavi-Roshan M, Mozafarihashjin M, Shoaibinobarian N, Ghorbani Z, Salari A, Savarrakhsh A, Hekmatdoost A: Evaluating the use of novel atherogenicity indices and insulin resistance surrogate markers in predicting the risk of coronary artery disease: a case ‒control investigation with comparison to traditional biomarkers . Lipids Health Dis 2022, 21 (1):126. Li X, Lu L, Chen Y, Liu B, Liu B, Tian H, Yang H, Guo R: Association of atherogenic index of plasma trajectory with the incidence of cardiovascular disease over a 12-year follow-up: findings from the ELSA cohort study . Cardiovasc Diabetol 2025, 24 (1):124. Lin Y, Lv X, Shi C, Wang T, Jin Z, Jin Q, Gu C: Association between atherogenic index of plasma and future cardiovascular disease risk in middle-aged and elderly individuals with cardiovascular-kidney-metabolic syndrome stage 0-3 . Front Endocrinol (Lausanne) 2025, 16 :1540241. Martínez-Hervás S, Vinué A, Núñez L, Andrés-Blasco I, Piqueras L, Real JT, Ascaso JF, Burks DJ, Sanz MJ, González-Navarro H: Insulin resistance aggravates atherosclerosis by reducing vascular smooth muscle cell survival and increasing CX3CL1/CX3CR1 axis . Cardiovasc Res 2014, 103 (2):324-336. Tao LC, Xu JN, Wang TT, Hua F, Li JJ: Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations . Cardiovasc Diabetol 2022, 21 (1):68. Liu H, Wang S, Wang J, Guo X, Song Y, Fu K, Gao Z, Liu D, He W, Yang LL: Energy metabolism in health and diseases . Signal Transduct Target Ther 2025, 10 (1):69. Welsh P, Preiss D, Lloyd SM, de Craen AJ, Jukema JW, Westendorp RG, Buckley BM, Kearney PM, Briggs A, Stott DJ et al : Contrasting associations of insulin resistance with diabetes, cardiovascular disease and all-cause mortality in the elderly: PROSPER long-term follow-up . Diabetologia 2014, 57 (12):2513-2520. Vargas-Vázquez A, Fermín-Martínez CA, Antonio-Villa NE, Fernández-Chirino L, Ramírez-García D, Dávila-López G, Díaz-Sánchez JP, Aguilar-Salinas CA, Seiglie JA, Bello-Chavolla OY: Insulin resistance potentiates the effect of remnant cholesterol on cardiovascular mortality in individuals without diabetes . Atherosclerosis 2024, 395 :117508. Altun Y, Balcı HD, Aybal N: Associations of the atherogenic index of plasma with insulin resistance and inflammation . Rev Assoc Med Bras (1992) 2024, 70 (11):e20240991. Hernández JL, Baldeón C, López-Sundh AE, Ocejo-Vinyals JG, Blanco R, González-López MA: Atherogenic index of plasma is associated with the severity of Hidradenitis Suppurativa: a case-control study . Lipids Health Dis 2020, 19 (1):200. Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9361985","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619968659,"identity":"1788d306-f9ea-4eca-bcab-a61b00e5fec7","order_by":0,"name":"Kun Xue","email":"","orcid":"","institution":"Qingdao Medical College of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Xue","suffix":""},{"id":619968660,"identity":"9173c7fb-553b-4e8b-b1b7-43b36553b48b","order_by":1,"name":"Haibin Dong","email":"","orcid":"","institution":"Yantai Yuhuangding 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01:57:32","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9361985/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9361985/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106726460,"identity":"f299b64f-2860-47f1-98ea-d00bcceee370","added_by":"auto","created_at":"2026-04-12 18:36:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1615746,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant selection from the CHARLS.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9361985/v1/9602aeb6d8b59271fb294b3e.jpg"},{"id":106637403,"identity":"1f183330-118f-42cd-b301-72dfe36c296c","added_by":"auto","created_at":"2026-04-10 17:01:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4713353,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves for incident cardiovascular disease according to the combined categories of AIP and IR surrogate indices.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9361985/v1/0a80a9de29a5b4f6d8ab4d50.jpg"},{"id":106637405,"identity":"1b26a852-f5b8-4ee7-97e6-871d4f8a3343","added_by":"auto","created_at":"2026-04-10 17:01:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1385313,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analyses of the associations between IR surrogate indices and incident cardiovascular disease according to AIP strata. Spline analyses were adjusted for age, sex, smoking, drinking, education level, marital status, Scr, hs-CRP, UA, BUN.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9361985/v1/288b0ba2f4542f19f33f4ef7.jpg"},{"id":106637404,"identity":"e6b1b2d3-3226-445c-8d0d-02f33012d156","added_by":"auto","created_at":"2026-04-10 17:01:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1377663,"visible":true,"origin":"","legend":"\u003cp\u003eSeven-year time-dependent ROC curves for prediction of incident cardiovascular disease. The basic model was adjusted for age, sex, smoking, drinking, education level, marital status, Scr, hs-CRP, UA, BUN. * indicates significantly improved predictive performance versus the basic model by DeLong's test (P \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9361985/v1/2ea58ff8d53efc09a40c5148.jpg"},{"id":106637406,"identity":"1b00417c-88f6-4a5e-b530-e8d8866e9e9f","added_by":"auto","created_at":"2026-04-10 17:01:25","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2189293,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analyses of the bidirectional relationships between AIP and IR surrogate indices in relation to cardiovascular disease. Adjusted for age, sex, smoking, drinking, education level, marital status, Scr, hs-CRP, UA, BUN. *P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9361985/v1/cd1f077d8e9e4a9d45805986.jpg"},{"id":106637407,"identity":"b6fc2c1e-59b2-427b-833c-43c7af0dbe42","added_by":"auto","created_at":"2026-04-10 17:01:25","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1286405,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the associations between the combined categories of AIP and IR surrogate indices and cardiovascular disease. Multivariate models were adjusted for age, sex, smoking, drinking, education level, marital status, Scr, hs-CRP, UA, BUN, with the exception of the stratification variable\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9361985/v1/f5e8a036614f1c92bbb83c0c.jpg"},{"id":106727756,"identity":"0a106dc1-3018-4662-b735-af79d565be1c","added_by":"auto","created_at":"2026-04-12 18:40:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19346369,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9361985/v1/01070713-8d36-4b41-b240-0d50229cea75.pdf"},{"id":106637401,"identity":"810a09b2-fd75-4f71-9c57-a71d6f05f359","added_by":"auto","created_at":"2026-04-10 17:01:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":688565,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9361985/v1/9fe356e25f651664a0808437.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eJoint associations of the atherogenic index of plasma and surrogate indices of insulin resistance with incident cardiovascular disease in middle-aged and older Chinese adults\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite improvements in medical and public health methods, cardiovascular disease (CVD) has been the primary cause of mortality and disability worldwide for decades[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Global Burden of Disease (GBD) research has continuously pointed out that CVD has a great impact on global health, among which ischemic heart disease and stroke are the main causes of this problem[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. From 1990 to now, the number of people suffering from CVD has more than doubled, and the death toll has increased from 13.1\u0026nbsp;million in 1990 to 19.2\u0026nbsp;million in 2023[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. To reduce the global CVD burden, we must focus on controlling these risk factors and adopt targeted public health strategies and actions.\u003c/p\u003e \u003cp\u003eInsulin resistance(IR) has been increasingly recognized as an important contributor to CVD[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], which is mainly related to vascular endothelial metabolism [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], inflammatory[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and dysfunction[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In extensive epidemiological research, various surrogate markers such as the triglyceride-glucose index (TyG), metabolic score for insulin resistance (METS-IR), and estimated glucose disposal rate (eGDR) are commonly utilized to assess CVD risk associated with IR[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. At the same time, the atherogenic index of plasma (AIP), an indicator of atherogenic dyslipidemia, has also been linked to cardiovascular outcomes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It is worth noting that there may be close interaction between AIP and IR. A cross-sectional study found that AIP showed an inverse L-shaped association with IR[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, one study found that in the presence of IR, an elevated TG/HDL-C ratio was as effective as the commonly used total cholesterol/HDL-C ratio in predicting CHD risk[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, AIP has been associated with diabetes[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and metabolic syndrome[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], suggesting its potential value in predicting diabetic complications and informing early intervention strategies. Considering the close biological relationship between IR and atherogenic dyslipidemia, a better understanding of their joint influence on the risk of CVD may help to find the risk earlier. However, prospective evidence on whether AIP provides complementary information beyond established IR surrogate indices for incident CVD remains limited.\u003c/p\u003e \u003cp\u003eTherefore, we used data from the China Health and Retirement Longitudinal Study (CHARLS) to investigate the associations of AIP and three surrogate indices of IR with incident CVD, as well as their joint associations. We further compared their incremental predictive value and potential mediating pathways.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eCHARLS used a multistage probability sampling design to achieve a sample that represents the nation. A total of 17,708 adults aged 45 and above were enrolled in the baseline survey, spanning 450 communities within 150 counties or districts across 28 provinces in China. The study obtained permission from the Peking University Ethics Review Committee (IRB00001052-11015), and all subjects provided written informed consent.\u003c/p\u003e \u003cp\u003eA detailed description of the CHARLS data collection process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In order to ensure the integrity of the data, we excluded the participants who met the following conditions: (1) those who lacked the data of TyG, eGDR, METS-IR and AIP; (2) People who have a history of CVD in Wave1 stage or who have lost their follow-up; (3) Participants under the age of 45; (4) participants with less than 2 years follow-up; (5) People who lack CVD diagnosis data in 2013, 2015 or 2018.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eIn the CHARLS survey, the staff collected demographic characteristics, lifestyle, medical history, anthropometric data and laboratory indicators according to standard procedures after training. Through the structured questionnaire, details about age, gender, marital status, educational background, smoking and drinking habits, along with medical conditions like hypertension, diabetes, heart disease, and stroke diagnosed by physicians were collected. Weight and waist circumference (WC) are measured directly, while blood pressure is taken as the average of three consecutive readings.\u003c/p\u003e \u003cp\u003eQualified medical staff collected fasting venous blood samples and processed them according to the CHARLS protocol. After storage and cold chain transportation, the samples were sent to the central laboratory for biochemical analysis. They measured serum total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and fasting blood glucose (FPG) by enzymatic method, while high-sensitivity C-reactive protein (hs-CRP) was measured by immunoturbidimetry.\u003c/p\u003e\n\u003ch3\u003eCalculation of insulin resistance surrogate indices and AIP\u003c/h3\u003e\n\u003cp\u003eWe employ three established surrogate indicators to assess insulin resistance, derived from standard biochemical testing and anthropometric measurements[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The TyG index calculation is ln [TG (mg/dL) \u0026times; FPG (mg/dL)/2]. The formula for computation is 21.158\u0026thinsp;\u0026minus;\u0026thinsp;0.09 x WC (cm)\u0026thinsp;\u0026minus;\u0026thinsp;3.407 x hypertension (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0)\u0026thinsp;\u0026minus;\u0026thinsp;0.551 x HbA1c (%). METS-IR is determined by the formula ln[(2 x FPG (mg/dL))\u0026thinsp;+\u0026thinsp;TG (mg/dL)] x BMI (kg/m\u0026sup2;) / ln [HDL-C (mg/dL)]. The AIP calculation method is log10 (TG/HDL-C), with TG and HDL-C represented in molar concentrations.\u003c/p\u003e\n\u003ch3\u003eOutcome definition and ascertainment\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was incident cardiovascular disease (CVD), which was ascertained during the second to fourth follow-up surveys. According to previous CHARLS-based research[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], CVD was characterized as self-reported physician-diagnosed heart disease or stroke, or the utilization of prescription medication for these ailments. Participants were categorized as having incident cardiovascular disease if any of these events were initially reported during the follow-up period. The follow-up duration was determined from baseline to the initial report of a CVD incident.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eDemographic traits, lifestyle habits, clinical data, existing health conditions, and medication usage were used to select baseline covariates. Covariates included Covariates encompassed age, sex, smoking status, alcohol consumption, educational attainment, marital status, systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), high-sensitivity C-reactive protein (hs-CRP), uric acid (UA), Blood Urea Nitrogen (BUN), serum creatinine (Scr), hypertension, diabetes, and dyslipidemia. Hypertension was defined as a self-reported physician diagnosis, an SBP of 140 mmHg or above, a DBP of 90 mmHg or higher, or the use of antihypertensive medication[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Diabetes was defined as self-reported physician diagnosis, fasting blood glucose\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;126 mg/dL, HbA1c\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;6.5%, or use of antidiabetic medication[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Dyslipidemia was defined as self-reported physician diagnosis, use of lipid-lowering medication, or abnormal lipid levels, including TG\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;150 mg/dL, LDL-C\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;160 mg/dL, TC\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;240 mg/dL, or HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHandling of missing data\u003c/h2\u003e \u003cp\u003eSeveral variables had missing values, as shown in Supplementary \u003cb\u003eFig. S1\u003c/b\u003e. To reduce the impact of incomplete data, we used Multiple Imputation by Chained Equations to generate five imputed datasets, and estimates were pooled according to Rubin's rules.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDepending on their distribution, continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range, IQR), and comparisons were made using the Student's t test or Mann-Whitney U test. Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for comparability and categorical variables are given as numbers (percentages) and were compared using either the chi-square test or Fisher's exact test. Because no established clinical cut-off values are available for TyG, eGDR, METS-IR, or AIP, these indices were dichotomized at the median. For joint association analyses, participants were classified into four groups according to each IR surrogate index and AIP: low IR/low AIP, low IR/high AIP, high IR/low AIP, and high IR/high AIP. Because lower eGDR values indicate greater insulin resistance, participants with eGDR below the median were categorized as having high IR. Kaplan-Meier curves and the log-rank test were used to compare cumulative CVD incident among groups, with incidence rates estimated per 1,000 person-years. Cox proportional hazards models were employed to assess the associations between the aggregated AIP categories and each IR surrogate index for incident CVD, with outcomes reported as hazard ratios (HRs) and 95% confidence intervals (CIs). Schoenfeld residuals were employed to assess the proportional hazards assumption. Multicollinearity diagnostics (\u003cb\u003eTables S1\u0026ndash;S3\u003c/b\u003e) indicated that all generalized variance inflation factors (GVIFs) were below 5, signifying the absence of significant multicollinearity across the covariates[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. We established three models: Model 1 was unadjusted. Model 2 included adjustments for age, sex, smoking and drinking status, education level, and marital status. Model 3 incorporated additional adjustments for hs-CRP, UA, Scr, and BUN. Further analyses using restricted cubic splines (RCS) were conducted to explore potential nonlinear relationships between each IR surrogate index and the occurrence of CVD based on AIP strata.\u003c/p\u003e \u003cp\u003eTo see if the prediction is accurate, we use the 7-year time-dependent ROC curve to further examine the Cox model, and then use an area called time-dependent AUC to measure its distinguishing ability. We also use NRI and IDI methods to see if the new model predicts better than the basic model[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMediation analyses were performed using the mediation package and accelerated failure time models to explore potential bidirectional mediation patterns between AIP and IR surrogate indices in relation to time to incident CVD. Total, direct, and indirect effects were estimated, and the proportion mediated was calculated [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Subgroup analyses were performed to examine whether the associations between the combined categories of AIP and IR surrogate indices and incident CVD were consistent across strata of age, sex, BMI, smoking, drinking, hypertension, diabetes, and dyslipidemia. Effect modification was assessed by including interaction terms. Additive interaction was evaluated using the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (SI) with the interactionR package. Several sensitivity analyses were also made. We further repeated the joint association analyses in participants with complete data only. Index-specific sensitivity analyses were further conducted by additionally adjusting for selected clinical conditions according to the surrogate index evaluated, while avoiding adjustment for variables overlapping with the construction of that index. In addition, heart disease and stroke were analyzed separately as supplementary outcomes to assess whether the associations differed across specific cardiovascular endpoints.\u003c/p\u003e \u003cp\u003eAll the analysis was done with R software (version 4.4.3). Mediation package is used for mediation analysis, mice is used to deal with missing data, and survival package is used for Cox regression. P value less than 0.05 (two-sided test) means that the results are statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline characteristics of study participants\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e indicates that 749 of the 4,117 participants got new instances of cardiovascular disease during the follow-up period. In comparison to people without CVD, those with newly diagnosed CVD were older, predominantly female, and had a higher prevalence of hypertension, diabetes, and dyslipidemia. They also differed with respect to marital status and smoking status, and had higher BMI, blood pressure, WC, TC, LDL-C, FPG, and HbA1c levels. AIP, TyG, and METS-IR were higher, whereas HDL-C and eGDR were lower. No significant differences were observed for education level, drinking status, TG, Scr, UA, or hs-CRP; BUN was slightly lower in the CVD group.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of the participants classified by CVD\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNon-CVD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e58.08 (8.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e57.72 (8.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e59.72 (8.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2247 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1791 (53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e456 (60.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1870 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1577 (46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e293 (39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEducation level, n (%)\u003c/p\u003e\n \u003cp\u003eSenior high school and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e448 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e362 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e86 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eJunior high school and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3669 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3006 (89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e663 (88.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMarriage group, n (%)\u003c/p\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3717 (90.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3060 (90.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e657 (87.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e400 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e308 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e92 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSmoking, n (%)\u003c/p\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2541 (61.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2061 (61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e480 (64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e304 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e238 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e66 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1272 (30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1069 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e203 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDrinking, n (%)\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2539 (61.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2067 (61.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e472 (63.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1578 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1301 (38.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e277 (37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2542 (61.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2185 (64.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e357 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1575 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1183 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e392 (52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3500 (85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2890 (85.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e610 (81.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e617 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e478 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e139 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2330 (56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1935 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e395 (52.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1787 (43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1433 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e354 (47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e23.68 (3.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e23.50 (3.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e24.47 (4.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e129.56 (20.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e128.62 (20.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e133.80 (22.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e75.37 (11.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e74.99 (11.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e77.08 (12.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e84.29 (12.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e83.68 (12.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e87.02 (13.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTC (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e194.48 (38.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e193.86 (39.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e197.25 (37.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTG (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e131.90 (109.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e130.47 (110.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e138.29 (104.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.076\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHDL (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e51.32 (15.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e51.63 (15.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e49.91 (14.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLDL (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e118.05 (35.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e117.50 (34.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e120.54 (36.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFPG (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e109.97 (33.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e109.09 (31.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e113.96 (40.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHbA1C (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5.27 (0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.24 (0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5.38 (0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBUN (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e15.60 (4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15.68 (4.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e15.24 (3.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eScr (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.77 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.77 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.76 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUA (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.35 (1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.36 (1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4.31 (1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ehs-CRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.41 (6.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.34 (5.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.74 (7.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.35 (0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.34 (0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.39 (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTyG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e8.68 (0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8.66 (0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8.77 (0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eeGDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e9.37 (2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9.54 (2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8.58 (2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMETS-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e35.88 (8.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e35.49 (8.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e37.64 (9.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n (%).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAbbreviations: CVD, cardiovascular disease; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; AIP, atherogenic index of plasma; TyG, triglyceride-glucose; eGDR, estimate glucose disposal rate; METS-IR, metabolic score for insulin resistance; UA, uric acid; Scr, serum creatinine, hs-CRP, high-sensitivity C-reactive protein; BUN, Blood Urea Nitrogen.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eBaseline characteristics were compared between groups using \u0026chi;\u0026sup2; or analysis of variance or Kruskal\u0026ndash;Wallis rank sum test where appropriate.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation of TyG, eGDR, METS-IR, and AIP with the risk of incident CVD\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eTable S5\u003c/strong\u003e indicates that elevated AIP, TyG, and METS-IR levels were strongly correlated with an increased risk of CVD following comprehensive adjustment, while greater eGDR was significantly linked to a reduced risk. According to Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the relationships between the joint categories of IR surrogate indicators and AIP and the incidence of CVD varied. After full adjustment, participants in the highest TyG-AIP category had a 21% higher risk of incident CVD than those in the reference group (HR 1.21, 95% CI 1.03\u0026ndash;1.43). For the eGDR-AIP combination, excess risk became evident in the less favorable categories and was most pronounced in Group 4, where the risk of CVD was 82% higher than that in the reference group (HR 1.82, 95% CI 1.39\u0026ndash;2.37). For METS-IR-AIP, risk was elevated across Groups 2 to 4, with the highest adjusted HR observed in Group 3 (HR 1.58, 95% CI 1.25-2.00), while Group 4 also remained significantly associated with incident CVD (HR 1.43, 95% CI 1.19\u0026ndash;1.73). In addition, all three joint indicators showed significant trends across the combined categories. Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates clear separation in cumulative CVD incidence across the combined TyG-AIP, eGDR-AIP, and METS-IR-AIP groups, with the greatest divergence observed for eGDR-AIP and METS-IR-AIP (all log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCombined effects of IR surrogate indices and AIP on CVD risk\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\n \u003cp\u003eModel I\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\n \u003cp\u003eModel II\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\n \u003cp\u003eModel III\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTyG and AIP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.97, 1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.02, 1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e0.97, 1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGroup 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.85, 1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.81, 1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1.11, 1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGroup 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.19, 1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.15, 1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1.03, 1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eeGDR and AIP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.97, 1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.96, 1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e0.95, 1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGroup 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.63, 2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.51, 2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1.20, 2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGroup 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.91, 2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.77, 2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1.39, 2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eMETS-IR and AIP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.12, 1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.06, 1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1.03, 1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGroup 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.34, 2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.39, 2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1.25, 2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGroup 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.37, 1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.38, 1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1.19, 1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\n \u003cp\u003eModel I: non-adjusted\u003c/p\u003e\n \u003cp\u003eModel II: adjusted for age, sex, smoking status, drinking status, education level, marital status\u003c/p\u003e\n \u003cp\u003eModel III: further adjusted for hs-CRP, UA, Scr, BUN.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eIdentifying nonlinear connections\u003c/h2\u003e\n \u003cp\u003eTo identify possible nonlinear connections, we used multivariable-adjusted restricted cubic spline analyses to study how IR surrogate indices relate to new cases of CVD, divided by AIP level. In the low AIP stratum, significant overall associations were observed for all three indices, but there was no evidence of nonlinearity. There was a positive correlation between TyG and CVD risk, an inverse linear correlation with eGDR, and a positive linear correlation with METS-IR (all P values for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 and all P values for nonlinearity\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C). In people with elevated AIP, the relationships between TyG and METS-IR and the occurrence of CVD remained significant overall, although neither showed a statistically significant nonlinear component. In contrast, eGDR demonstrated a significant nonlinear association with incident CVD in the high AIP subgroup (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F). Further two-piecewise Cox regression analysis identified a significant inflection point for eGDR at 9.26 in the fully adjusted model (P for log-likelihood ratio test\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cstrong\u003eTable S4\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003ePredictive performance of IR surrogates and AIP for CVD\u003c/h2\u003e\n \u003cp\u003eAs presented in Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the basic model demonstrated limited predictive performance for 7-year incident CVD, with a time-dependent AUC of 0.578 (95% CI, 0.547\u0026ndash;0.609). The addition of AIP was associated with only a slight improvement in discrimination, increasing the AUC to 0.589 (95% CI, 0.559\u0026ndash;0.618). When IR surrogate indices were further incorporated, the magnitude of improvement differed across indices. The gain was most pronounced for eGDR, with the AUC increasing to 0.628 (95% CI, 0.598\u0026ndash;0.658), followed by METS-IR, with an AUC of 0.611 (95% CI, 0.581\u0026ndash;0.641). In contrast, TyG produced only a marginal increase in AUC to 0.594 (95% CI, 0.564\u0026ndash;0.624). These patterns were broadly consistent with the reclassification results shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Specifically, adding eGDR significantly improved reclassification (NRI 0.320, 95% CI 0.123 to 0.404; P\u0026thinsp;=\u0026thinsp;0.016) and discrimination (IDI 0.019, 95% CI 0.013 to 0.027; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas METS-IR showed a smaller but still significant improvement (NRI 0.178, 95% CI 0.088 to 0.311; P\u0026thinsp;=\u0026thinsp;0.008; IDI 0.010, 95% CI 0.002 to 0.023; P\u0026thinsp;=\u0026thinsp;0.012). TyG did not significantly improve NRI or IDI.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIncremental reclassification performance of AIP and IR indices for incident CVD\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNRI (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eIDI (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBasic\u0026thinsp;+\u0026thinsp;TyG vs Basic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.179 (-0.010 to 0.228)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.004 (-0.004 to 0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBasic+eGDR vs Basic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.320 (0.123 to 0.404)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.019 (0.013 to 0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBasic+METS-IR vs Basic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.178 (0.088 to 0.311)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.010 (0.002 to 0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBasic\u0026thinsp;+\u0026thinsp;AIP+TyG vs Basic\u0026thinsp;+\u0026thinsp;AIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.053 (-0.018 to 0.076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.001 (-0.012 to 0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBasic\u0026thinsp;+\u0026thinsp;AIP+eGDR vs Basic\u0026thinsp;+\u0026thinsp;AIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.310 (0.044 to 0.431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.016 (0.011 to 0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBasic\u0026thinsp;+\u0026thinsp;AIP+METS-IR vs Basic\u0026thinsp;+\u0026thinsp;AIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.233 (0.022 to 0.294)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.007 (0.001 to 0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eThe basic model was adjusted for age, sex, smoking status, drinking status, education level, marital status, BUN, Scr, UA, hs-CRP\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eMediation analyses of IR surrogates and AIP on CVD risk\u003c/h2\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, exploratory mediation analyses based on accelerated failure time models suggested that only eGDR and METS-IR showed evidence of mediation in the association between AIP and time to incident CVD. The mediating effect was much stronger for eGDR, which accounted for 49.62% of the association, whereas METS-IR accounted for only 2.69%. No significant mediation was observed for TyG. In the reverse direction, no significant mediation through AIP was detected for any of the three IR surrogate indices.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eSubgroup and sensitivity analyses\u003c/h2\u003e\n \u003cp\u003eSubgroup analyses showed that the associations of the combined TyG-AIP, eGDR-AIP, and METS-IR-AIP categories with incident CVD were broadly consistent across strata of age, sex, smoking status, drinking status, BMI, hypertension, dyslipidemia, diabetes (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cstrong\u003eand Fig.S2\u003c/strong\u003e). In general, participants in the high-risk combined category had a higher risk of CVD than those in the reference category across most subgroups. After completely adjusting for covariates, no notable interactions were found concerning age, sex, smoking habits, drinking habits, BMI, hypertension, dyslipidemia, or diabetes (P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). To confirm the reliability of the main results, multiple sensitivity analyses were conducted. First, all analyses were repeated in complete-case participants (n\u0026thinsp;=\u0026thinsp;4,103). Second, index-specific sensitivity analyses were further conducted by additionally adjusting for selected clinical conditions according to the surrogate index evaluated, while avoiding adjustment for variables overlapping with the construction of that index. Third, heart disease and stroke were analyzed separately as supplementary outcomes (n\u0026thinsp;=\u0026thinsp;4,117). Significantly, these analyses produced results that aligned with the primary findings (\u003cstrong\u003eTable S6-S9\u003c/strong\u003e). Exploratory interaction analyses are presented in Supplementary \u003cstrong\u003eTable S10\u003c/strong\u003e. No clear multiplicative or additive interaction was observed for AIP with TyG or eGDR, whereas AIP and METS-IR showed an antagonistic interaction on the multiplicative scale.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this national cohort of 4,117 middle-aged and older Chinese adults, higher AIP combined with a more adverse IR profile was associated with a greater risk of incident CVD, although the strength of this pattern differed across surrogate indices. The joint association was strongest for eGDR and AIP. AIP alone provided limited incremental predictive value, whereas eGDR substantially improved discrimination and reclassification; TyG contributed comparatively little. We also found that the association between AIP and incident CVD may be partly mediated by IR, particularly the component reflected by eGDR, whereas evidence for the reverse pathway was limited. Overall, these findings suggest that combined assessment of AIP and selected IR surrogate indices may improve identification of individuals at high risk of CVD, and that this pattern is robust across subgroup and sensitivity analyses.\u003c/p\u003e \u003cp\u003eOur findings are broadly in line with previous studies showing that surrogate markers of IR are associated with cardiovascular risk and that AIP is likewise linked to adverse cardiovascular outcomes. Earlier studies have reported significant associations of TyG, eGDR, and METS-IR with CVD [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], while accumulating evidence has also identified AIP as a marker of atherogenic dyslipidemia and cardiovascular risk[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. First, rather than evaluating these markers in isolation, we examined their joint associations and showed that the coexistence of an adverse IR profile and elevated AIP identified individuals at particularly high risk of incident CVD. Second, by comparing three commonly used IR surrogates within the same prospective cohort, we found that the eGDR-AIP combination showed the strongest association, whereas the corresponding patterns for METS-IR-AIP and especially TyG-AIP were less pronounced. These findings add to the limited prospective evidence on the joint assessment of AIP and established IR surrogates for incident CVD and suggest that the value of combining AIP with IR-related markers may vary according to the surrogate index used.\u003c/p\u003e \u003cp\u003eSeveral mechanisms may help explain the observed joint association of AIP and IR with incident CVD, as well as the stronger performance of eGDR in the present study. One critical pathway involves the promotion of atherosclerosis through reduced vascular smooth muscle cell survival and enhanced inflammatory signaling, such as activation of the CX3CL1/CX3CR1 axis, which may increase plaque vulnerability[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. IR is associated with an increase in inflammation, atherosclerosis, oxidative stress, and endothelial dysfunction, factors that lead to the development of CVD[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], particularly in younger individuals, indicating that IR not only acts independently but may also amplify other lipid-related risks[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], particularly in younger individuals, indicating that IR not only acts independently but also amplifies other lipid-related risks[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In addition, AIP has been reported to correlate with increased levels of glucose, insulin, and inflammatory markers such as C-reactive protein[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. When these abnormalities coexist, they may identify a more adverse cardiometabolic milieu than either marker alone. In our study, this pattern was most evident for eGDR, which may be because eGDR incorporates WC, hypertension, and HbA1c, and may therefore better capture the chronic metabolic burden relevant to cardiovascular damage than TyG or METS-IR. In summary, combined assessment of AIP and IR surrogate indices, especially eGDR, may improve early identification of middle-aged and older adults at elevated risk of incident CVD.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study has several strengths, including its prospective design, the nationally representative CHARLS cohort, and the simultaneous comparison of three IR surrogate indices in combination with AIP. In addition, we used a comprehensive analytic strategy that included joint association analyses, nonlinear modeling, prediction analyses, mediation analyses, and multiple sensitivity analyses, which strengthened the overall interpretation of the findings.\u003c/p\u003e \u003cp\u003eHowever, several limitations should be considered. First, because of the observational design, causal inferences cannot be made. Second, incident CVD identification relied on self-reported physician diagnoses or medication usage, which could lead to misclassification. Third, TyG, eGDR, and METS-IR are surrogate rather than gold-standard measures of IR, and all exposures were measured at baseline only. Fourth, despite adjustment for a wide range of potential confounders in the multivariable and subgroup analyses, the influence of residual confounding from unmeasured factors, including diet, physical activity, and socioeconomic status, cannot be completely ruled out. Fifth, the joint analyses relied on dichotomization at the median, which facilitated risk stratification but may also have led to some loss of information. Although restricted cubic spline analyses were performed as complementary analyses, the categorization-based results should still be interpreted with this limitation in mind. Finally, because our study was limited to middle-aged and older Chinese adults, additional studies are warranted to evaluate whether these findings can be generalized to other ethnic groups, age ranges, and populations with different lifestyle characteristics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, higher AIP and poor IR profiles were linked to incident CVD in this national cohort. Among the three indices, eGDR showed the strongest association with AIP, offering the best predictive value and mediation signal. These results suggest that combining AIP with specific IR markers, particularly eGDR, could enhance cardiovascular risk assessment in middle-aged and older adults.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTyG, triglyceride-glucose\u003c/p\u003e\n\u003cp\u003eeGDR, estimate glucose disposal rate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMETS-IR, metabolic score for insulin resistance\u003c/p\u003e\n\u003cp\u003eAIP, atherogenic index of plasma\u003c/p\u003e\n\u003cp\u003eHDL-C, High-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eLDL-C, Low-density lipoprotein cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCVD, Cardiovascular disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTG, Triglyceride\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTC, Total cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehs-CRP, High-sensitivity C-reactive protein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHbA1c, glycated hemoglobin\u003c/p\u003e\n\u003cp\u003eIR, Insulin resistance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBUN, Blood Urea Nitrogen\u003c/p\u003e\n\u003cp\u003eSBP, systolic blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDBP, diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eBMI, Body mass index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFPG, fasting plasma glucose\u003c/p\u003e\n\u003cp\u003eScr, serum creatinine\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUA, uric acid\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKX conceptualized and designed the study. KX, JS, HD, YW, YR, CW, and LG carried out research. XK, KF and BX performed the data analysis. XK composed the initial draft, while LZ and TD aided in data evaluation and revisions. All authors endorsed the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor this investigation, the researchers drew upon the CHARLS database. They extend their sincere appreciation to the CHARLS research team and acknowledge the indispensable role played by every individual who contributed to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for the research detailed in this article was provided by the Shandong Province Traditional Chinese Medicine Science and Technology Project, with Grant Numbers M20250608.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHARLS website (http://charls.pku.edu.cn/) provides access to the data that underpins the findings of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Biomedical Ethics Review Committee of Peking University conducted and approved this study, adhering to the Declaration of Helsinki principles. The survey received the nod with approval number IRB00001052-11015, and the blood collection process was approved under IRB00001052-11014. Each participant penned their informed consent in writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm the research involved no commercial or financial interests that might present conflicts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eXu S, Liu Y, Zhu M, Chen K, Xu F, Liu Y: \u003cstrong\u003eGlobal burden of atherosclerotic cardiovascular disease attributed to lifestyle and metabolic risks\u003c/strong\u003e. \u003cem\u003eSci China Life Sci\u0026nbsp;\u003c/em\u003e2025, \u003cstrong\u003e68\u003c/strong\u003e(9):2739-2754.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGlobal, Regional, and National Burden of Cardiovascular Diseases and Risk Factors in 204 Countries and Territories, 1990-2023\u003c/strong\u003e. \u003cem\u003eJ 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Nevertheless, evidence regarding their combined value in cardiovascular disease (CVD) risk stratification remains limited. We therefore examined the joint associations of AIP and three IR surrogate indices with incident CVD.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this prospective research, 4,117 adults aged 45 years or more from the China Health and Retirement Longitudinal Study (CHARLS) were included, all of whom were free of cardiovascular disease at the beginning. The median was used to split AIP and each IR surrogate index into two categories. Cox models, restricted cubic spline analyses, 7-year time-dependent ROC analyses, integrated discrimination improvement, net reclassification improvement, mediation analyses, subgroup analyses, and sensitivity analyses were conducted.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDuring follow-up, 749 participants developed incident CVD. In fully adjusted models, the highest combined categories of TyG-AIP, eGDR-AIP, and METS-IR-AIP were associated with higher CVD risk:1.21, 95% CI: 1.03, 1.43; 1.82, 95% CI: 1.39, 2.37 and 1.43, 95% CI:1.19, 1.73. Among participants with high AIP, only eGDR showed a significant nonlinear association with incident CVD. AIP alone provided limited improvement in discrimination, whereas eGDR showed the greatest predictive gain, increasing the 7-year AUC from 0.578 to 0.628; METS-IR showed a smaller improvement, while TyG added little. Reclassification analyses showed a similar pattern. Mediation analyses indicated that the association between AIP and incident CVD was partly mediated by eGDR and, to a lesser extent, by METS-IR, whereas no significant mediation was observed for TyG. No significant multiplicative or additive interaction was observed between AIP and TyG or eGDR, whereas AIP and METS-IR showed a significant antagonistic interaction on the multiplicative scale.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHigher AIP and adverse IR profiles were jointly associated with incident CVD. Among the three surrogate indices, eGDR showed the most informative overall profile. Combined assessment of AIP and eGDR may improve cardiovascular risk stratification.\u003c/p\u003e","manuscriptTitle":"Joint associations of the atherogenic index of plasma and surrogate indices of insulin resistance with incident cardiovascular disease in middle-aged and older Chinese adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 17:01:17","doi":"10.21203/rs.3.rs-9361985/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"58975f1e-92e2-40c8-8c0c-53aa0ebd3ac4","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65976579,"name":"Cardiac \u0026 Cardiovascular Systems"}],"tags":[],"updatedAt":"2026-04-10T17:01:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 17:01:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9361985","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9361985","identity":"rs-9361985","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}