Changes in the Estimated Glucose Disposal Rate and Incident Cardiovascular Disease in Patients with Cardiovascular–Kidney–Metabolic Syndrome Stages 0–3: A Prospective Cohort Study in China

Changes in the Estimated Glucose Disposal Rate and Incident Cardiovascular Disease in Patients with Cardiovascular–Kidney–Metabolic Syndrome Stages 0–3: A Prospective Cohort Study in China | 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 Changes in the Estimated Glucose Disposal Rate and Incident Cardiovascular Disease in Patients with Cardiovascular–Kidney–Metabolic Syndrome Stages 0–3: A Prospective Cohort Study in China Jia Liu, Wendong Xu, Yue Zhang, Yuan Jia, Shiru Bai, Lu Er, Rongpin Du This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7331938/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Nov, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 10 You are reading this latest preprint version Abstract Background and aims Cardiovascular–kidney–metabolic syndrome (CKM) significantly increases the burden of cardiovascular disease (CVD), particularly in China,which has a rapidly aging population.The estimated glucose disposal rate (eGDR) is a reliable indicator for assessing insulin resistance (IR),but its dynamic changes and association with the risk of new-onset CVD in patients with CKM syndrome have not been fully elucidated.The aim of this study was to investigate the associations between dynamic changes in and cumulative of eGDR (cumeGDR) and the risk of new-onset CVD in Chinese adults with CKM syndrome. Methods ： A total of 2862 patients with CKM syndrome (stages 0–3) without CVD at baseline from the China Health and Retirement Longitudinal Study (CHARLS) were enrolled. K-means clustering was used to measure the change in eGDR from 2012 to 2015, and the cumulative eGDR level was calculated. Logistic regression, restricted cubic splines (RCS), and subgroup analysis were used to explore the potential associations between changes in the eGDR and the risk of new-onset CVD (including heart disease and stroke) in patients with CKM syndrome stages 0–3. Results ： During the 3-year follow-up period, 404 (14.1%) CVD events occurred, including 254 heart disease cases and 177 stroke cases. After adjusting for confounding factors, compared with the group with persistently high eGDR level (Class 1), the groups with significantly decreased eGDR level (Class 2) and persistently low eGDR level (Class 3) had a significantly increased CVD risk (Class 2: OR = 1.82 [1.36–2.45],P<0.001;Class 3: OR = 1.90 [1.41–2.56],P<0.001). Further RCS regression analysis revealed a negative linear association between the cumulative eGDR and CVD risk(P for overall <0.001, nonlinear P=0.922). Conclusion ： Persistently low eGDR level are associated with an increased risk of new-onset CVD in those with CKM syndrome stages 0–3. Continuous dynamic monitoring of the eGDR may help identify high-risk individuals with CKM syndrome stages 0–3 and provide critical evidence for early intervention. Cardiovascular disease Cardiovascular–kidney–metabolic syndrome Insulin resistance Estimated glucose disposal rate CHARLS Prospective cohort study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cardiovascular–kidney–metabolic syndrome (CKM) was formally defined by the American Heart Association (AHA) in 2023.It is a systemic disease characterized by the pathophysiological interactions among obesity, diabetes, chronic kidney disease (CKD), and cardiovascular disease[1]. This pathophysiological interaction significantly increases the risk of serious adverse cardiac, renal, and metabolic events[2], leading to high incidences of morbidity, mortality, and health care burdens worldwide.Approximately 90% of adults worldwide have stage 1 or higher CKM syndrome[1,3]. This widespread distribution of a high-risk population means that effective early intervention strategies have significant public health value.In China, this challenge is particularly acute, where CVD accounts for more than 45% of all-cause mortality, and the clinical burden of CKM syndrome is driven mainly by CVD[4]. Individuals with CKM syndrome stages 0–3 are often asymptomatic but require focused prevention of CVD events; however, traditional models often fail to comprehensively integrate metabolic, renal, and vascular biomarkers, which exacerbates the difficulty of early identification and precise risk stratification[1,5]. Insulin resistance is a core driver of CKM syndrome progression[6], exacerbating metabolic dysregulation[7], endothelial damage[8], and organ fibrosis[9,10]. The hyperinsulinemic–euglycemic clamp (HIEC) technique is the gold standard for detecting IR;however, the invasiveness, complexity, time-consuming nature, and high cost of this technique make it impractical for routine clinical practice and widespread application[11]. Homeostasis model assessment of insulin resistance (HOMA-IR), a surrogate marker for IR, is calculated on the basis of fasting insulin values, which are not commonly tested in routine clinical practice[12]. Therefore, there is an urgent need to find alternative markers of IR that are easily accessible and applicable in routine clinical practice. Recently, the estimated glucose disposal rate (eGDR), which is calculated from readily available clinical parameters, including hemoglobin A1c (HbA1c), hypertension, and waist circumference (WC), has emerged as a more convenient surrogate marker for IR[13] and is more suitable for large-scale application and routine clinical practice. Compared with HOMA-IR, eGDR performs better in predicting cardiovascular risk, all-cause mortality, and cardiovascular mortality[14–16]. A higher eGDR indicates lower level of IR, whereas a lower eGDR indicates higher level of IR. eGDR has been shown in a recent study to identify those at high risk of CVD in patients with CKM stages 0–3[4,17]. However, a single eGDR measurement reflects a static IR status and fails to capture the dynamic changes in IR over time and their potential impact. Given the central driving role and possible time-varying nature of IR in CKM syndrome, systematically evaluating the relationship between the longitudinal trajectory of eGDR change and CVD risk is crucial for achieving more precise dynamic risk stratification and disease management.Unfortunately, there is an extreme paucity of research in this area. Therefore, the aim of this study was to utilize prospective cohort data from the CHARLS to explore the associations between the dynamic trajectory of eGDR after baseline and the risk of new-onset CVD in Chinese adult patients with CKM syndrome. In addition, to assess the impact of long-term IR exposure, we also examined the association between cumulative eGDR level and the risk of new-onset CVD in Chinese patients with CKM syndrome. Methods 1. Data source and study population The data used in this study were derived from the China Health and Retirement Longitudinal Study, which is a nationally representative longitudinal cohort designed to investigate the health, socioeconomic, and behavioral characteristics of middle-aged and elderly individuals (≥45 years) in China. The CHARLS baseline survey was initiated in 2011 (Wave 1), covering 150 counties/districts and 450 villages/communities in 28 provinces across the country and utilizing a stratified multistage probability sampling methodology with a total of 17,708 respondents from 10,257 households, representing the diversity of the middle-aged and elderly population in China. Follow-up surveys were conducted every 2–3 years, including those in 2013 (Wave 2), 2015 (Wave 3), 2018 (Wave 4), and 2020 (Wave 5). In addition to questionnaires, CHARLS collected venous blood samples from participants in Wave 1 and Wave 3 to measure biomarkers, including glucose, lipids, and other biomarkers, which provide important data to support the study of metabolic diseases. Detailed information on sampling methods, anthropometric measurements and blood biomarker information for CHARLS has been previously documented in previous publications. The datasets from Wave 1 (baseline, 2011) and Wave 3 (follow-up, 2015) were extracted, considering the available blood test data. Each participant was required to fast overnight and their blood was collected and analyzed at the center by medical personnel from the Chinese Center for Disease Control and Prevention according to standard protocols. All research laboratories were standardized and accredited. The fasting blood glucose (FBG) concentration was measured via the enzyme colorimetric method, and hemoglobin A1c (HbA1c) was evaluated with boric acid affinity high-performance liquid chromatography(HPLC)[18]. We first included 11,847 participants who completed blood tests in Wave 1 (2011), as blood samples were only collected in Waves 1 and 3. A total of 2,862 participants were included in the final study after meeting the following exclusion criteria: (1) inability to define the CKM syndrome stage in Wave 1, (2) lack of CVD status in subsequent follow-up, (3) inability to define eGDR in both Wave 1 and Wave 3, (4) lack of demographic information, and (5) preexisting CVD before 2015. Figure 1 presents the study population selection process. The CHARLS study was approved by the Ethics Review Board of Peking University Biomedical (IRB00001052–11015). All participants signed written informed consent forms before the start of the study. The principles outlined in the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) were followed in the current study[19]. Detailed information on the CHARLS data is accessible on its official website (http://charls.pku.edu.cn/en)[18]. Data assessment and definitions Assessment of exposure The eGDR was computed utilizing the following formula: eGDR = 21.158 − (0.09 × WC) − (3.407 × HT) − (0.551 × HbA1c)[13], where WC is waist circumference (cm) and HT is hypertension status (yes = 1, no = 0) The cumulative of eGDR was calculated as follows: CumeGDR=[(eGDR 2012 +eGDR 2015 )/2]*time(2015-2012) Assessment of outcomes The outcome of this study was the incidence of CVD, including heart disease and stroke. Consistent with previous studies[20–22], incident CVD events were assessed by standardized questions:"Have you been diagnosed by a doctor as having a stroke/heart disease (including a heart attack, coronary heart disease, angina pectoris, congestive heart failure, or other heart problems)?" or "Are you currently receiving any of the following treatments (taking traditional Chinese medicines/taking modern Western medicines/other treatments/none of the above) for stroke/heart disease or its complications?". Participants who reported "yes" to a physician diagnosis of heart disease or stroke or indicated receiving specific treatment for heart disease or stroke were defined as having cardiovascular disease. Definition of CKM syndrome stages 0 to 4 The stages of CKM syndrome from 0 to 4 are classified in accordance with the AHA Presidential Advisory Statement[1]. The CKM syndrome stages 0–4 are specified as follows: stage 0: no CKM syndrome health risk factors; stage 1: abdominal obesity and/or prediabetes; stage 2: metabolic disorders(type 2 diabetes, hypertension, and high triglycerides) or renal disorders; stage 3: subclinical CVD in the context of CKM syndrome; and stage 4: clinical CVD (coronary heart disease, heart failure, stroke, peripheral artery disease, atrial fibrillation) in the context of CKM syndrome. Subclinical CVD is defined as having a ≥20% 10-year CVD risk or high-risk CKD according to the American Heart Association (AHA) Predicting Risk of CVD Events (PREVENT) equations. Data collection This study collected the following data: (1) demographic characteristics: gender, age, educational status (primary school or below, secondary school, college or above), hukou status (urban/rural), and marital status (married or other); (2) lifestyle factors: smoking status (never, former, current) and drinking status (never, former, current); (3) physical measurements: systolic blood pressure (SBP), diastolic blood pressure (DBP), and body mass index (BMI, kg/m²); (4) medical history: self-reported physician-diagnosed hypertension, diabetes, dyslipidemia, kidney disease, liver disease, lipid-lowering therapy, antihypertensive therapy, and diabetes treatment. Hypertension was defined as an average SBP ≥140 mmHg, or an average DBP ≥90 mmHg at baseline, or current use of antihypertensive medication, or self-reported history of hypertension[23]. Diabetes was defined as a fasting blood glucose level ≥126 mg/dl (7 mmol/L) and/or a random blood glucose level ≥200 mg/dl (11.1 mmol/L) and/or an HbA1c level ≥6.5% at baseline and/or self-reported history of diabetes or current use of anti-diabetic medication[24]. Other medical conditions were ascertained on the basis of self-reported history or receipt of any condition-specific treatment. (5) Laboratory examination: serum creatinine (Scr), blood urea nitrogen (BUN), uric acid (UA), estimated glomerular filtration rate (eGFR), fasting blood glucose (FBG), HbA1c, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) level. The eGFR was determined using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation[25]. Handling of missing variables Additional file 1: Table S1 illustrates the distribution of missing data for the included study participants. To maximize the sample size, we used multiple interpolation with chained equations (MICE) and implemented this multiple interpolation process through the “MICE” package, despite the small proportion of missing data. Statistical analysis Normally distributed continuous variables are expressed as the means ± standard deviations (SDs), whereas nonnormally distributed variables are presented as the medians with interquartile ranges (IQRs). Categorical variables are reported as frequencies with percentages (%). Group comparisons were performed with χ² tests for categorical variables, ANOVA for normally distributed continuous variables, and the Kruskal‒Wallis test for nonparametric data. First, participants were classified into three groups using K-means clustering on the basis of their eGDR values from 2012 to 2015. K-means clustering is a technique that aims to divide N observations into K clusters, and K-means clustering effectively captures subtle changes in the variation in the dynamic eGDR and uses the elbow method to determine the optimal number of clusters[26]. Once the best-fit model was determined, participants were assigned to their most likely class. However, this approach is partly subjective and relies on the visual interpretation of the “elbow” . As shown in Figure 2A, the rate of reduction in within-cluster variance (distortion) declined significantly beyond K = 3, indicating an inflection point. At this threshold, adding more clusters provided negligible explanatory power (the ‘elbow’ criterion). Accordingly, as shown in Figure 2B, the participants were stratified into three classes. Class 1 was characterized by persistently high eGDR level, with eGDR ranging from 10.96±1.38 in 2012 to 10.59±0.81 in 2015, indicating optimal eGDR control. Class 2 was characterized by initially high eGDR that significantly decreased to low level, with eGDR ranging from 10.18±1.28 in 2012 to 7.27±1.11 in 2015, suggesting impaired eGDR control. Class 3 was characterized by persistently low eGDR level, with eGDR ranging from 6.59±0.99 in 2012 to 6.26±1.36 in 2015, indicating poor eGDR control. Additionally, we used continuous cumeGDR and quartiles of the cumeGDR group to assess the associations between long-term changes in the eGDR and CVD incidence. Logistic regression analyses were conducted to evaluate the associations between cumeGDR, eGDR control level and CVD events, with odds ratios (ORs) and 95% confidence intervals (95% CIs) calculated across three models. The crude model was unadjusted for covariates. Model 1 was adjusted for age and gender. Model 2 included adjustments for all covariates included in Model 1 and in addition was adjusted for hukou status, education status, marital status, smoking status, drinking status, TC, LDL, eGFR, diabetes, lipid-lowering therapy, antihypertensive therapy, and diabetes treatment. Multicollinearity was tested using the variance inflation factor (VIF) method, with a VIF ≥ 5 indicating the presence of multicollinearity. Additional file 2: Table S2 indicating evidence of no significant multicollinearity. Furthermore, restricted cubic spline (RCS) regression analysis was employed to examine linear and dose‒response relationships between cumeGDR and CVD incidence in participants with CKM syndrome. Additionally, various subgroup and interaction analyses were performed to detect potential effect modifications. The participants were stratified into subgroups by age (<60 years vs. ≥60 years), gender (male vs. female), marital status (married vs. other), hukou status (urban/rural), and education status (<middle school vs. ≥middle school). Sensitivity analysis were performed to assess the robustness of the primary results. First, we applied a logistic regression model that excluded participants with missing values for covariates to mitigate the potential influence of missing values on the primary results. Second, the data were reanalyzed after excluding participants with cancer at baseline (2015) to assess the potential impact of preexisting cancer on the observed associations. Finally, we extended the follow-up of these participants until 2020 to test the stability of the results over a longer time period. All the statistical analyses were performed using R software version 4.3.1 (http://www.R-project.org/), and a two-sided P <0.05 was considered statistically significant. Results 1. Population characteristics Figure 1 illustrates the inclusion and exclusion criteria used in this study. Specifically, we included 17,708 participants in Wave 1 of the CHARLS cohort. Of these, 10,455 participants were unable to be classified into CKM syndrome stage in Wave 1, 232 participants were excluded because of incomplete follow-up data, 286 participants were excluded because they had CVD at baseline, 3,870 participants were excluded because of missing eGDR data in Waves 1 and 3, and 3 participants were excluded because of a lack of demographic data. A total of 2,862 participants with a mean age of 57 years (51–63 years) were included in the final study, of which 1,563 (54.6%) were female. The average eGDR of the participants was 9.42 in 2012 and 8.56 in 2015. The cumeGDR for the entire cohort was 28.22. Table 1 summarizes the baseline characteristics of participants with CKM syndrome stages 0–3 in the three groups on the basis of the level of eGDR control (Classes 1–3). These classes were significantly different in terms of age, gender, education level, smoking, drinking, CKM syndrome stage, BMI, SBP, DBP, comorbidities (hypertension, diabetes, dyslipidemia), medication status (antihypertensive drugs, hypoglycemic drugs, lipid-lowering drugs), and biochemical markers (HbA1c, FBG, TG, TC, HDL-C, LDL-C, UA, eGFR). Specifically, Class 3 represents the most severely metabolically dysregulated patients, with an older age, higher BMI and blood pressure, more comorbidities, and more severe biochemical abnormalities than the other groups. Table 1 summarizes the baseline characteristics of the participants with stages 0–3 CKM syndrome in the 3 groups on the basis of the level of eGDR control (Classes 1–3). In addition, baseline characteristics stratified by cumeGDR quartiles are detailed in Table S3. 2. Associations between eGDR changes and new-onset CVD During the 3-year follow-up (from Wave 3 in 2015 to Wave 4 in 2018), 404 participants (14.1%) had CVD events, namely, 254 had heart attacks (8.9%) and 177 had strokes (6.2%). Logistic analysis was applied to study the relationship between eGDR changes and new-onset CVD. As shown in Table 2, different IR control states reflected by eGDR were observed after individuals were grouped via K-means clustering analysis. Compared with Class 1, Classes 2 and 3 had a significantly greater risk of new-onset CVD (Class 2: OR 1.82, 95% CI 1.36–2.45, P<0.001; Class 3: OR 1.90, 95% CI 1.41–2.56, P<0.001), heart disease (Class 2: OR 2.01, 95% CI 1.42–2.86, P<0.001; Class 3: OR 1.78, 95% CI 1.23–2.55), P=0.002, and stroke (Class 2: OR 1.67, 95% CI 1.06–2.61, P=0.025; Class 3: OR 1.95, 95% CI 1.26–3.02, P=0.003). Cumulative eGDR was also used to assess eGDR changes. When the first quartile (the group with the lowest cumulative level) was used as a reference, with increasing cumeGDR, the event rates of new CVD, heart disease, and stroke gradually decreased (p for trend <0.001). These results remained consistent after correction for potential confounders. Restricted cubic spline (RCS) models were applied to assess the dose‒response relationships between cumeGDR and new-onset CVD, heart disease, and stroke. As shown in Figure 3, the fully adjusted RCS model revealed a negative linear correlation between cumeGDR and new-onset CVD (P for overall< 0.001, P for nonlinearity= 0.922). In addition, as shown in Figure4, the RCS model revealed a negative linear correlation between cumeGDR and incident heart disease and stroke (heart disease: P for overall = 0.021, P for nonlinearity= 0.825; stroke: P for overall = 0.007, P for nonlinearity= 0.565). 3. Subgroup analysis To further investigate the relationships between eGDR changes and cumeGDR and new-onset CVD, a series of subgroup analyses were conducted. As shown in Figures 5 and 6, subgroups stratified by age, gender, hukou status, education, and marital status did not influence the relationships between eGDR changes and cumeGDR and new-onset CVD (all P>0.05 for interaction). 4. Sensitivity analysis Sensitivity analyses confirmed the robustness of the primary results. First, after removing participants with missing covariates in the fully adjusted model, the association between the control level of eGDR and the incidence of CVD remained consistent with our study results (Class 2: OR 1.82, 95% CI 1.38–2.49, P<0.001; Class 3: OR 1.95, 95% CI 1.44–2.62, P<0.001). Furthermore, a negative correlation persisted between cumulative eGDR and the new-onset of CVD (p for trend <0.001), as detailed in Additional File 4: Table S4. Second, after excluding individuals with cancer at the baseline in 2015, poor eGDR control level was associated with an increased risk of new-onset CVD (Class 2: OR 1.83, 95% CI 1.36–2.45, P<0.001; Class 3: OR 1.92, 95% CI 1.42–2.57, P<0.001) and the association between cumeGDR and CVD was consistent with the primary findings (p for trend <0.001) (Additional file 4: Table S5). Finally, we extended the follow-up of these participants until 2020, and consistent with our study, the risk of CVD continued to increase in the group with a significant decrease in eGDR and in the group with persistently low level (Class 2: OR 2.06, 95% CI 1.56–2.72, P<0.001; Class 3: OR 2.31, 95% CI 1.75–3.04, P<0.001). There was a negative correlation between cumeGDR and the incidence of CVD (p for trend <0.001)(Additional file 4: Table S6). Discussion In this study, we dynamically assessed the longitudinal trajectory of eGDR in patients with CKM syndrome stages 0–3 by using K-means clustering analysis, revealing a significant association with cardiovascular disease risk. Our study revealed that individuals with a persistently low eGDR had a significantly greater risk of new-onset CVD in this high-risk population with CKM syndrome, and this study further confirmed a significant linear negative correlation between the cumeGDR and the risk of CVD in patients with CKM syndrome stages 0–3. This finding indicates that eGDR is not just a static marker of IR; its dynamic trajectory and long-term cumeGDR are assessment metrics that better capture the nature of disease progression. Focusing on the CKM syndrome population with disturbed multisystem interactions, this study provides key evidence for the early identification of high-risk individuals and is of significant clinical value in establishing dynamic risk prediction models and realizing precise interventions for high-risk individuals. IR is characterized by reduced sensitivity to the physiological effects of insulin, which can lead to abnormal glucose and lipid metabolism, directly drive atherosclerosis, and induce endothelial dysfunction, chronic inflammation, and oxidative stress, accelerating arteriosclerosis and thrombosis[ 27 ]. In addition, IR is significantly associated with the risk of cardiovascular disease, chronic kidney disease, and metabolic disorders[ 7 , 28 , 29 ]. Particularly in the CKM syndrome population, IR drives cardiac, renal, and metabolic damage simultaneously, creating a vicious cycle[ 30 ] in which all three diseases often co-occur, and having more than one of these diseases at the same time exponentially increases the risk of death, primarily due to cardiovascular disease[ 31 ]; thus, early intervention reduces the risk of progression, and attention needs to be paid to evaluating IR. Although the hyperinsulinemic–euglycemic clamp (HIEG) and HOMA-IR are considered reliable metrics for assessing IR, their complex measurement techniques limit their practical application[ 11 , 12 ]. The triglyceride‒glucose(TyG) index does not integrate clinical parameters (e.g., blood pressure, obesity), and its predictive stability is insufficient. To overcome the limitations of the above measurements, the eGDR was developed by the Pittsburgh Epidemiology of Diabetes Complications (EDC) study and further validated in type 1 diabetes[ 13 ]. The eGDR integrates metabolic, obesity, and glycemic control parameters and is superior to a single indicator, with a correlation of r = 0.63 with the HIEG clamp[ 32 ], and its predictive efficacy exceeds that of six alternative IR indicators (TyG index, TyG-waist circumference, TyG-body mass index, TyG-waist-to-height ratio, triglyceride-to-high density lipoprotein cholesterol ratio, and metabolic score for insulin resistance)[ 33 , 34 ]. On the basis of these findings, eGDR seems to be a reasonable alternative indicator for IR estimation. The eGDR has been proven to effectively predict CVD events and stroke risk in different populations, including those with diabetes, those without diabetes, and the general population[ 15 , 35 ]. Notably, in the CKM syndrome population, each 1-unit increase in eGDR was associated with a 9% reduction in the risk of CVD (HR 0.91, 95% CI 0.88–0.93)[ 4 ]. Given that patients in the early stages of CKM syndrome (stages 0–3) generally have visceral obesity and chronic inflammation and that IR is the core driving factor, this study focused on this population. Previous studies have focused mostly on single-point measurements of baseline eGDR and have not considered the impact of eGDR fluctuations during follow-up. In contrast, the progression of CKM syndrome essentially reflects the continuous drift of metabolic parameters rather than a simple stage transition. Thus, capturing dynamic changes in the eGDR may be more capable of reflecting disease progression and risk than single-point measurements are. We applied K-means clustering to analyze the dynamic changes in eGDR and compared with the static limitation of the traditional quartile method. This method was used to identify three types of clinical phenotypes: persistently high, significantly decreased, and persistently low. A recent study in the general population revealed that participants with consistently low eGDR were associated with a significantly greater risk of CVD risk[ 36 ], confirming the importance of dynamic assessment. By identifying specific eGDR changes (such as persistently high, significantly decreased, and persistently low), it is possible to identify high-risk individuals more accurately, optimize risk stratification, and reflect the effectiveness of interventions through dynamic monitoring. For example, treatment with glucose-lowering drugs (such as glucagon-like peptide-1 receptor agonist, GLP-1RA) improves insulin sensitivity[ 37 ]. Previous studies have mostly assessed β-cell function by HOMA-IR, and in the future, we can use acquired metrics, such as eGDR, to provide a quantitative bridge for drug efficacy assessment. Our study used K-means clustering analysis to divide CKM syndrome participants into three subgroups on the basis of their eGDR from 2012 to 2015. The results revealed that the eGDR in all subgroups tended to decrease, indicating that the degree of IR increased with age, which was consistent with the findings of previous studies[ 36 ] and which may be attributed to physiological decline, increased adipose tissue, decreased skeletal muscle, and a lack of effective interventions. Compared with Class 1 (persistently high group), the risk of new-onset CVD was significantly greater in Class 2 (significantly decreased group) and Class 3 (persistently low group). When further classified on the basis of the quartiles of cumeGDR, participants with the lowest cumeGDR had the highest risk of new-onset CVD. After adjusting for potential confounding factors, the above associations remained robust. The RCS curve revealed a negative linear relationship between cumeGDR and new-onset CVD. These findings indicate that eGDR level are independently associated with new-onset CVD. From a mechanistic perspective, the eGDR, as an alternative marker for IR, not only reflects reduced insulin sensitivity but also captures multidimensional dysregulation, including glucose and lipid metabolism disorders, endothelial dysfunction, chronic inflammation, and oxidative stress[ 38 – 40 ]. These interrelated pathological processes collectively contribute to the onset and progression of CVD in patients with CKM syndrome. First, IR increases the production of very low-density lipoprotein (VLDL), and its metabolic product, residual lipoprotein, is deposited in the vascular endothelium, accelerating plaque formation[ 41 ].Elevated free fatty acids (FFAs) in the IR state lead to increased ceramide and diacylglycerol(DAG), inhibiting glucose transporter-4 (GLUT-4) membrane translocation in skeletal muscle and adipose tissue, interfering with glucose uptake, and forming a vicious cycle of hyperglycemia and hyperinsulinemia[ 42 ]. The persistent lipotoxic environment leads to abnormal accumulation of FFAs in cardiomyocytes, directly damaging myocardial contractile function by inhibiting mitochondrial β-oxidation and increasing reactive oxygen species (ROS) production[ 42 ].Second, the core damage caused by IR to the vascular system lies in disrupting the dual-pathway balance of insulin signaling, leading to endothelial dysfunction[ 43 ]. The PI3K/A-t pathway mediated by insulin for vasodilation is inhibited, leading to decreased endothelial nitric oxide synthase (eNOS) activity and reduced NO production, whereas the MAPK pathway is overly activated, resulting in increased endothelin-1 (ET-1), causing sustained vasoconstriction and endothelial dysfunction[ 43 ]. Notably, this process synergistically exacerbates endothelial damage with lipotoxicity. A reduction in NO weakens the antioxidant capacity of blood vessels, whereas FFA deposition directly damages the endothelium. The combined effects of these two factors lay an important pathological foundation for atherosclerosis[ 44 , 45 ]. Finally, recent studies have shown that IR and chronic inflammation have a bidirectional promoting relationship, accelerating the progression of CKM syndrome[ 46 ]. IR triggers the release of FFAs and proinflammatory adipokines (such as leptin and resistin) from adipose tissue, activates monocyte differentiation into M1 macrophages, and upregulates the expression of proinflammatory factors such as TNF-α, IL-6, and IL-12[ 46 , 47 ]. This systemic chronic inflammation not only directly damages the vascular endothelium but also further inhibits tyrosine phosphorylation of insulin receptor substrate (IRS) by activating the classic IKKβ-NF-κB molecular pathway, exacerbating insulin resistance[ 46 , 47 ]. Moreover, a hyperglycemic environment promotes the accumulation of advanced glycation end products (AGEs)[ 48 ] and activates NADPH oxidase, leading to excessive production of reactive oxygen species (ROS). ROS play multiple destructive roles in the pathogenesis of CVD. ROS directly oxidize low-density lipoprotein (LDL) to form ox-LDL, promote foam cell formation, and increase plaque instability[ 49 ]; they also impair myocardial cell mitochondrial function and reduce energy production efficiency[ 50 , 51 ]. ROS activate the NLRP3 inflammasome, promoting the maturation and release of proinflammatory factors such as IL-1; at the same time, inflammatory cell infiltration generates more ROS. These multiple interrelated mechanisms collectively lead to early atherosclerosis in patients with CKM syndrome.Our findings indicate that dynamic IR monitoring can comprehensively reflect the pathophysiological processes in patients with CKM syndrome and that the cumulative effects of these pathophysiological processes are reflected in the cumeGDR. Persistently low eGDR level indicate long-term exposure to metabolic disorders, chronic inflammation, and oxidative stress, all of which synergistically increase the risk of CVD in patients with CKM syndrome. Therefore, dynamic monitoring of eGDR changes can be used to assess patients' metabolic status comprehensively and is helpful for the early identification of high-risk populations for CVD. Our study has several advantages, including the use of a large, nationally representative CHARLS cohort and the construction of a dynamic change trajectory of eGDR through K-means clustering, which overcomes the limitations of traditional single measurements and accurately identifies the high-risk population of metabolic deterioration in patients with stages 0–3 CKM syndrome. However, our study has certain limitations. First, only two blood tests were performed, which prevented a more detailed characterization of eGDR. When eGDR is only evaluated at a specific time, its continuous changes over time are not fully captured. Future studies with more frequent measurements could provide deeper insights into the dynamic nature of metabolic disorders. Second, although we adjusted for multiple confounding factors, residual confounding factors, such as dietary patterns and socioeconomic factors, cannot be completely ruled out and may affect the causal inference between eGDR and CVD. Our findings are particularly applicable to middle-aged and elderly populations in China and do not cover young CKM syndrome patients whose metabolic characteristics and dynamic changes in eGDR may present different patterns. The diagnosis of cardiovascular disease is based on self-reported physician diagnoses rather than imaging (CT or MRI), which introduces potential misclassification bias. However, the reliability of self-reported cardiovascular events has been validated, providing some assurance of reliability. Finally, our study adopted strict exclusion criteria (incomplete CKM syndrome data or missing CVD records), which were consistent with previous studies. Excluding certain participants may not cause bias in the study results. As a comprehensive indicator reflecting insulin resistance and metabolic function, the eGDR can be calculated with only routine clinical data. It is low-cost, easily accessible, and suitable for grassroots settings such as community health centers and rural clinics. This study demonstrated that its dynamic changes (such as persistently low level and significant decreases) can significantly increase the risk of CVD, which is particularly applicable to primary medical institutions with limited health care resources and can be used as a tool for the initial screening of high-risk populations. Dynamic changes in eGDR can occur earlier than clinical symptoms can, suggesting deterioration of metabolic function. Early interventions such as lifestyle modification and intensive management of blood glucose/blood pressure in patients with persistently low level or significantly declining level may halt the progression of CVD and avoid the high-cost burden of late-stage treatment. The cumeGDR integrates long-term metabolic status and is more predictive than a single measurement. Its linear association with CVD simplifies risk assessment models. Clinicians can intuitively determine the long-term risk level of patients by periodically calculating the cumeGDR. Based on the results and limitations of this study, future research could focus on the following directions: (1) Firstly, it is necessary to verify the change validate the generalisability and predictive value of the eGDR change trajectories and cumulative eGDR metrics in a wider range of populations (encompassing different ethnicities and age groups). Building on the findings and limitations of this study, future research could focus on the following directions: (1) First, it is necessary to validate the generalizability and predictive value of eGDR change and cumeGDR in a wider range of populations, such as those of different races and age groups. Moreover, the integration of multiomics data, such as genomics and metabolomics data, to analyze the molecular mechanisms behind the dynamic changes in eGDR will provide key insights into understanding its biological basis. (2) On this basis, a CVD risk prediction model should be developed and validated, integrating eGDR and other relevant factors to improve the identification of high-risk individuals. (3) Ultimately, it is critical to conduct prospective interventional studies to validate whether increasing eGDR levels through specific strategies, such as lifestyle interventions for weight loss or targeted drug therapies, is effective in reducing the risk of subsequent CVD events, thereby providing direct causal evidence for the development of prevention strategies. Conclusion Poorly controlled eGDR level significantly increase the risk of new-onset CVD in patients with CKM syndrome stages 0–3. Dynamic monitoring of eGDR changes can optimize the risk stratification of CVD, and these findings help to more accurately identify high-risk populations and provide key evidence for early intervention to reduce the burden of cardiovascular disease. Abbreviations CKM Cardiovascular-kidney-metabolic CVD Cardiovascular diseases eGDR estimated glucose disposal rate IR Insulin resistance CHARLS China Health and Retirement Longitudinal Study CKD Chronic Kidney disease AHA American Heart Association HIEC hyperinsulinemic-euglycemic clamp HOMA-IR Homeostasis model assessment of insulin resistance HbA1c Hemoglobin A1c WC Waist circumference FBG Fasting blood glucose HPLC High performance liquid chromatography STROBE Strengthening the Reporting of Observational Studies in Epidemiology HT Hypertension PREVENT Predicting Risk of CVD Events SBP Systolic blood pressure DBP Diastolic blood pressure BMI Body mass index Scr Serum creatinine BUN Blood urea nitrogen UA Uric acid TC Total cholesterol TG Triglyceride HDL-C High-density lipoprotein cholesterol LDL-C Low-density lipoprotein cholesterol CKD-EPI Chronic Kidney Disease Epidemiology Collaboration MICE Multiple interpolation with chained equations SD Standard deviation IQR Interquartile range OR Odds ratios CI Confidence interval Q Quartiles VIF Variance inflation factor RCS Restricted cubic spline HR Hazard ratio TyG Triglyceride-glucose GLP-1RA Glucagon-like peptide-1 receptor agonist VLDL Very low-density lipoprotein FFAs Free fatty acids DAG Diacylglycerol GLUT-4 Glucose transporter-4 ROS Reactive oxygen species KDIGO Kidney Disease Improvement Global Outcomes PI3K-Akt Phosphatidylinositol kinase/protein kinase B eNOS endothelial nitric oxide synthase MAPK Mitogen-activated protein kinase ET-1 Endothelin-1 TNF-αTumor Necrosis Factor-α IL Interleukin IRS Insulin receptor substrate NF-κB Nuclear factor-kappa B AGEs Advanced glycation end products NADPH Nicotinamide adenine dinucleotide phosphate NLRP NOD-like receptor thermal protein domain assiciated protein Declarations Ethics approval and consent to participate The study was approved by the ethics review committee (institutional review board) of Peking University. All participants provided official written consent for their participation. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during this study are available in the China Health and Retirement Longitudinal Study repository [http:// charls.pku.edu.cn]. Competing interests The authors declare no conflict of interest. Clinical trial number Not applicable. Author contributions JL, RPD, and WDX conceived and designed the study. JL, YZ, LE and YJ conducted the research. JL and SRB analyzed the data. JL wrote the manuscript. All authors read and approved of the final manuscript. Acknowledgements The author thanks all CHALRS members for their contributions, as well as the participants who provided data. We also thank AJE academic editorial team (https://www.aje.cn/) for language editing service. Author details 1 Department of Cardiology, Hebei General Hospital, NO.348 Heping West Road, Shijiazhuang, Hebei, 050000, China. References Ndumele CE, Neeland IJ, Tuttle KR, Chow SL, Mathew RO, Khan SS, et al. A Synopsis of the Evidence for the Science and Clinical Management of Cardiovascular-Kidney-Metabolic (CKM) Syndrome: A Scientific Statement From the American Heart Association. Circulation. 2023;148:1636–64. Marassi M, Fadini GP. The cardio-renal-metabolic connection: a review of the evidence. Cardiovasc Diabetol. 2023;22:195. Lu L, Chen Y, Liu B, Li X, Wang J, Nie Z, et al. Association between cumulative changes of the triglyceride glucose index and incidence of stroke in a population with cardiovascular-kidney-metabolic syndrome stage 0–3: a nationwide prospective cohort study. Cardiovasc Diabetol. 2025;24:202. Tan Z, Zhou D, Tang Y, Huo G. Association between estimated glucose disposal rate and incident cardiovascular disease in a population with Cardiovascular-Kidney-Metabolic syndrome stages 0–3: insights from CHARLS. Front Cardiovasc Med. 2025;12:1537774. Tao S, Yu L, Li J, Wu J, Huang X, Xie Z, et al. Insulin resistance quantified by estimated glucose disposal rate predicts cardiovascular disease incidence: a nationwide prospective cohort study. Cardiovasc Diabetol. 2025;24:161. Ding X, Tian J, Chang X, Liu J, Wang G. Association between remnant cholesterol and the risk of cardiovascular-kidney-metabolic syndrome progression: insights from the China Health and Retirement Longitudinal Study. Eur J Prev Cardiol. 2025;zwaf248. Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuñiga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17:122. Chang Y, Kim CK, Kim M-K, Seo W-K, Oh K. Insulin resistance is associated with poor functional outcome after acute ischemic stroke in non-diabetic patients. Sci Rep. 2021;11:1229. Qin W, Weng J. Hepatocyte NLRP3 interacts with PKCε to drive hepatic insulin resistance and steatosis. Sci Bull. 2023;68:1413–29. Ghafoury R, Malek M, Ismail-Beigi F, Khamseh ME. Role of Residual Inflammation as a Risk Factor Across Cardiovascular-Kidney-Metabolic (CKM) Syndrome: Unpacking the Burden in People with Type 2 Diabetes. Diabetes Ther Res Treat Educ Diabetes Relat Disord; 2025. Muniyappa R, Lee S, Chen H, Quon MJ. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab. 2008;294:E15–26. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28:412–9. Williams KV, Erbey JR, Becker D, Arslanian S, Orchard TJ. Can clinical factors estimate insulin resistance in type 1. diabetes? Diabetes. 2000;49:626–32. Song J, Ma R, Yin L. Associations between estimated glucose disposal rate and arterial stiffness and mortality among US adults with non-alcoholic fatty liver disease. Front Endocrinol. 2024;15:1398265. Zhang Z, Zhao L, Lu Y, Xiao Y, Zhou X. Insulin resistance assessed by estimated glucose disposal rate and risk of incident cardiovascular diseases among individuals without diabetes: findings from a nationwide, population based, prospective cohort study. Cardiovasc Diabetol. 2024;23:194. Guo L, Zhang J, An R, Wang W, Fen J, Wu Y, et al. The role of estimated glucose disposal rate in predicting cardiovascular risk among general and diabetes mellitus population: a systematic review and meta-analysis. BMC Med. 2025;23:234. Tian J, Chen H, Luo Y, Zhang Z, Xiong S, Liu H. Association between estimated glucose disposal rate and prediction of cardiovascular disease risk among individuals with cardiovascular-kidney-metabolic syndrome stage 0–3: a nationwide prospective cohort study. Diabetol Metab Syndr. 2025;17:58. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort Profile: The China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43:61–8. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61:344–9. Li H, Zheng D, Li Z, Wu Z, Feng W, Cao X, et al. Association of Depressive Symptoms With Incident Cardiovascular Diseases in Middle-Aged and Older Chinese Adults. JAMA Netw Open. 2019;2:e1916591. Xie W, Zheng F, Yan L, Zhong B. Cognitive Decline Before and After Incident Coronary Events. J Am Coll Cardiol. 2019;73:3041–50. Gao K, Cao L-F, Ma W-Z, Gao Y-J, Luo M-S, Zhu J, et al. Association between sarcopenia and cardiovascular disease among middle-aged and older adults: Findings from the China health and retirement longitudinal study. EClinicalMedicine. 2022;44:101264. Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur Heart J. 2018;39:3021–104. Yu J, Yi Q, Chen G, Hou L, Liu Q, Xu Y, et al. The visceral adiposity index and risk of type 2 diabetes mellitus in China: A national cohort analysis. Diabetes Metab Res Rev. 2022;38:e3507. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604–12. Setiawan KE, Kurniawan A, Chowanda A, Suhartono D. Clustering models for hospitals in Jakarta using fuzzy c-means and k-means. Procedia Comput Sci. 2023;216:356–63. Li M, Chi X, Wang Y, Setrerrahmane S, Xie W, Xu H. Trends in insulin resistance: insights into mechanisms and therapeutic strategy. Signal Transduct Target Ther. 2022;7:216. Kosmas CE, Bousvarou MD, Kostara CE, Papakonstantinou EJ, Salamou E, Guzman E. Insulin resistance and cardiovascular disease. J Int Med Res. 2023;51:3000605231164548. Nakashima A, Kato K, Ohkido I, Yokoo T. Role and Treatment of Insulin Resistance in Patients with Chronic Kidney Disease: A Review. Nutrients. 2021;13:4349. Ndumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, et al. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. Circulation. 2023;148:1606–35. Larkin H. Here’s What to Know About Cardiovascular-Kidney-Metabolic Syndrome, Newly Defined by the AHA. JAMA. 2023;330:2042–3. Mertens J, Roosens L, Braspenning R, Vandebeeck J, Francque S, De Block C. The 13C Glucose Breath Test Accurately Identifies Insulin Resistance in People With Type 1 Diabetes. J Clin Endocrinol Metab. 2025;110:e432–42. Hong D, Li X, Sheng G, Yang H, Wang W, Zou Y. Comparing the impact of cumulative insulin resistance surrogates exposure on stroke: optimizing prevention strategies. Lipids Health Dis. 2025;24:158. Dong B, Chen Y, Yang X, Chen Z, Zhang H, Gao Y, et al. Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0–3 and the development of a machine learning prediction model: a nationwide prospective cohort study. Cardiovasc Diabetol. 2025;24:163. Yao J, Zhou F, Ruan L, Liang Y, Zheng Q, Shao J, et al. Association between estimated glucose disposal rate control level and stroke incidence in middle-aged and elderly adults. J Diabetes. 2024;16:e13595. Yan L, Zhou Z, Wu X, Qiu Y, Liu Z, Luo L, et al. Association between the changes in the estimated glucose disposal rate and new-onset cardiovascular disease in middle-aged and elderly individuals: A nationwide prospective cohort study in China. Diabetes Obes Metab. 2025;27:1859–67. Berra CC, Rossi MC, Mirani M, Ceccarelli Ceccarelli D, Romano C, Sassi L, et al. Real world effectiveness of subcutaneous semaglutide in type 2 diabetes: A retrospective, cohort study (Sema-MiDiab01). Front Endocrinol. 2022;13:1099451. Tan J, Li X, Dou N. Insulin Resistance Triggers Atherosclerosis: Caveolin 1 Cooperates with PKCzeta to Block Insulin Signaling in Vascular Endothelial Cells. Cardiovasc Drugs Ther. 2024;38:885–93. Luo C, Han S, Shen X, Wu H, Zhou J, Liu B. Sex-specific associations between estimated glucose disposal rate and cognitive decline in middle-aged and older adults in China: a longitudinal cohort study. Front Aging Neurosci. 2025;17:1544352. Li Y, Li H, Chen X, Liang X. Association between various insulin resistance indices and cardiovascular disease in middle-aged and elderly individuals: evidence from two prospectives nationwide cohort surveys. Front Endocrinol. 2024;15:1483468. Molecular Regulation and Therapeutic Targeting of VLDL Production in Cardiometabolic Disease. - Cellular and Molecular Gastroenterology and Hepatology [Internet]. [cited 2025 June 13]. Available from: https://www.cmghjournal.org/article/S2352-345X(24)00164-4/fulltext Engin AB. What Is Lipotoxicity? In: Engin AB, Engin A, editors. Obes Lipotoxicity [Internet]. Cham: Springer International Publishing; 2017 [cited 2025 July 2]. pp. 197–220. Available from: https://doi.org/10.1007/978-3-319-48382-5_8 King GL, Park K, Li Q. Selective Insulin Resistance and the Development of Cardiovascular Diseases in Diabetes: The 2015 Edwin Bierman Award Lecture. Diabetes. 2016;65:1462–71. Di Pino A, DeFronzo RA. Insulin Resistance and Atherosclerosis: Implications for Insulin-Sensitizing Agents. Endocr Rev. 2019;40:1447–67. Hill MA, Yang Y, Zhang L, Sun Z, Jia G, Parrish AR, et al. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metabolism. 2021;119:154766. Garg SS, Kushwaha K, Dubey R, Gupta J. Association between obesity, inflammation and insulin resistance: Insights into signaling pathways and therapeutic interventions. Diabetes Res Clin Pract [Internet]. 2023 [cited 2025 June 13];200. Available from: https://www.diabetesresearchclinicalpractice.com/article/S0168-8227(23)00453-9/abstract Chen L, Chen R, Wang H, Liang F. Mechanisms Linking Inflammation to Insulin Resistance. Int J Endocrinol. 2015;2015:508409. Chaudhuri J, Bains Y, Guha S, Kahn A, Hall D, Bose N, et al. The Role of Advanced Glycation End Products in Aging and Metabolic Diseases: Bridging Association and Causality. Cell Metab. 2018;28:337–52. Wu X, Pan X, Kang J, Huang Y, Ren J, Pan J, et al. Ferulic acid inhibits ox-LDL-induced ferroptosis and apoptosis in RAW 264.7 cells via the HIF-1 signaling pathway. Front Pharmacol. 2025;16:1524736. Ye W, Han K, Xie M, Li S, Chen G, Wang Y, et al. Mitochondrial energy metabolism in diabetic cardiomyopathy: Physiological adaption, pathogenesis, and therapeutic targets. Chin Med J (Engl). 2024;137:936–48. Cadenas S. ROS and redox signaling in myocardial ischemia-reperfusion injury and cardioprotection. Free Radic Biol Med. 2018;117:76–89. Tables Table 1 Baseline characteristics according to eGDR by K-means clustering analysis in CKM syndrome stages 0–3 Characteristic Overall (N = 2862) Class1 (N = 1388) Class2 (N = 569) Class3 (N = 905) P value Age,years 57.00 [51.00, 63.00] 55.00 [49.00, 61.00] 58.00 [52.00, 65.00] 59.00 [53.00, 65.00] < 0.001 Gender,% 0.038 Female 1299 (45.4) 619 (44.6) 285 (50.1) 395 (43.6) Male 1563 (54.6) 769 (55.4) 284 (49.9) 510 (56.4) Marital status,% 0.336 Married 2509 (87.7) 1229 (88.5) 491 (86.3) 789 (87.2) Others 353 (12.3) 159 (11.5) 78 (13.7) 116 (12.8) Education,% 0.001 College or above 73 ( 2.6) 32 ( 2.3) 16 ( 2.8) 25 ( 2.8) Elementary school or below 1922 (67.2) 882 (63.5) 399 (70.1) 641 (70.8) Middle school 867 (30.3) 474 (34.1) 154 (27.1) 239 (26.4) Hukou status,% 0.071 Agriculture 2462 (86.0) 1215 (87.5) 484 (85.1) 763 (84.3) Others 400 (14.0) 173 (12.5) 85 (14.9) 142 (15.7) Smoking status 0.001 Current Smoker 872 (30.5) 422 (30.4) 203 (35.7) 247 (27.3) Former Smoker 204 ( 7.1) 81 ( 5.8) 44 ( 7.7) 79 ( 8.7) Never Smoked 1786 (62.4) 885 (63.8) 322 (56.6) 579 (64.0) Drinking status < 0.001 Current Drinker 895 (31.3) 418 (30.1) 198 (34.8) 279 (30.8) Former Drinker 190 ( 6.6) 70 ( 5.0) 35 ( 6.2) 85 ( 9.4) Never Drinker 1777 (62.1) 900 (64.8) 336 (59.1) 541 (59.8) CKM syndrome stages, % < 0.001 0 114 ( 4.0) 108 ( 7.8) 6 ( 1.1) 0 ( 0.0) 1 356 (12.4) 302 (21.8) 54 ( 9.5) 0 ( 0.0) 2 833 (29.1) 345 (24.9) 175 (30.8) 313 (34.6) 3 1559 (54.5) 633 (45.6) 334 (58.7) 592 (65.4) BMI,kg/m 2 23.28 [21.09, 25.80] 22.16 [20.39, 24.14] 23.61 [20.93, 26.41] 25.23 [23.00, 27.52] < 0.001 SBP,mmHg 125.33 [114.00, 140.33] 116.33 [108.33, 125.67] 126.67 [118.33, 135.67] 145.00 [133.33, 156.67] < 0.001 DBP,mmHg 74.33 [67.33, 82.33] 69.67 [63.67, 75.67] 74.33 [68.00, 80.67] 83.33 [75.33, 90.67] < 0.001 Diabetes,% < 0.001 No 2702 (94.4) 1361 (98.1) 534 (93.8) 807 (89.2) Yes 160 ( 5.6) 27 ( 1.9) 35 ( 6.2) 98 (10.8) Hypertension,% < 0.001 No 1810 (63.2) 1303 (93.9) 480 (84.4) 27 (3.0) Yes 1052 (36.8) 85 ( 6.1) 89 ( 15.6) 878 (97.0) Dyslipidemia,% < 0.001 No 2627 (91.8) 1329 (95.7) 534 (93.8) 764 (84.4) Yes 235 ( 8.2) 59 ( 4.3) 35 ( 6.2) 141 (15.6) Kidney disease,% 0.051 No 2770 (96.8) 1332 (96.0) 554 (97.4) 884 (97.7) Yes 92 ( 3.2) 56 ( 4.0) 15 ( 2.6) 21 ( 2.3) Liver disease,% 0.323 No 2787 (97.4) 1354 (97.6) 549 (96.5) 884 (97.7) Yes 75 ( 2.6) 34 ( 2.4) 20 ( 3.5) 21 ( 2.3) Lipid-lowering treatment,% < 0.001 No 2747 (96.0) 1362 (98.1) 553 (97.2) 832 (91.9) Yes 115 ( 4.0) 26 ( 1.9) 16 ( 2.8) 73 ( 8.1) Antihypertensive treatment,% < 0.001 No 2431 (84.9) 1385 (99.8) 540 (94.9) 506 (55.9) Yes 431 (15.1) 3 ( 0.2) 29 ( 5.1) 399 (44.1) Hypoglycemic treatment,% < 0.001 No 2760 (96.4) 1371 (98.8) 546 (96.0) 843 (93.1) Yes 102 ( 3.6) 17 ( 1.2) 23 ( 4.0) 62 ( 6.9) HbA1c, % 5.10 [4.90, 5.40] 5.10 [4.80, 5.30] 5.20 [4.90, 5.50] 5.20 [5.00, 5.60] < 0.001 FBG, mg/dL 102.60 [95.22, 112.32] 100.44 [93.42, 108.54] 103.86 [95.58, 115.56] 104.94 [98.28, 118.80] < 0.001 TG, mg/dL 105.32 [74.34, 155.76] 95.58 [69.03, 134.52] 101.78 [71.68, 154.88] 126.56 [87.61, 184.08] < 0.001 TC, mg/dL 191.37 [168.17, 216.11] 185.57 [164.30, 209.25] 191.37 [169.33, 218.43] 198.33 [175.13, 224.61] < 0.001 HDL-c, mg/dL 49.10 [40.59, 59.92] 51.42 [42.53, 61.86] 49.48 [39.43, 61.47] 46.39 [38.27, 55.28] < 0.001 LDL-c, mg/dL 115.21 [94.33, 139.18] 111.92 [92.78, 134.15] 115.98 [92.40, 140.34] 120.62 [97.81, 144.98] < 0.001 BUN,mg/dL 14.96 [12.50, 17.93] 14.97 [12.58, 17.79] 14.90 [12.38, 18.01] 15.01 [12.52, 18.15] 0.988 Creatinine,mg/dL 0.73 [0.64, 0.86] 0.75 [0.63, 0.85] 0.73 [0.64, 0.86] 0.73 [0.66, 0.88] 0.296 Uric acid, mg/dL 4.17 [3.46, 5.01] 4.02 [3.35, 4.78] 4.24 [3.48, 5.04] 4.38 [3.64, 5.31] < 0.001 eGFR, mL/min/1.73m² 96.51 [86.87, 103.80] 98.17 [87.49, 104.77] 96.38 [87.60, 103.87] 94.37 [84.93, 101.35] < 0.001 Cumulative eGDR 28.22 [21.64, 32.26] 32.32 [30.62, 33.95] 26.18 [24.52, 27.78] 19.52 [17.49, 21.43] < 0.001 CVD < 0.001 No 2458 (85.9) 1264 (91.1) 476 (83.7) 718 (79.3) Yes 404 (14.1) 124 ( 8.9) 93 (16.3) 187 (20.7) Heart disease,% < 0.001 No 2608 (91.1) 1308 (94.2) 504 (88.6) 796 (88.0) Yes 254 ( 8.9) 80 ( 5.8) 65 (11.4) 109 (12.0) Stroke,% < 0.001 No 2685 (93.8) 1340 (96.5) 532 (93.5) 813 (89.8) Yes 177 ( 6.2) 48 ( 3.5) 37 ( 6.5) 92 (10.2) BMI, body Mass Index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin A1c;FBG, fasting blood glucose; TG, triglycerides; TC, total cholesterol;HDL-c,high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; BUN,blood urea nitrogen; eGFR, estimated glomerular filtration rate; eGDR, estimated glucose disposal rate; CVD, cardiovascular diseases. Table 2 Logistic analysis for the association between different eGDR change and CVD. Crude model Model1 Model2 eGDR OR(95%CI) P value OR(95%CI) P value OR(95%CI) P value Change in the eGDR 1 Ref Ref Ref 2 1.99 (1.49–2.66) < 0.001 1.90 (1.42–2.55) < 0.001 1.82 (1.36–2.45) < 0.001 3 2.65 (2.08–3.40) < 0.001 2.43 (1.90–3.12) < 0.001 1.90 (1.41–2.56) < 0.001 Cumulative eGDR Q1 Ref Ref Ref Q2 0.60 (0.46–0.79) < 0.001 0.62 (0.47–0.81) < 0.001 0.75 (0.56–1.01) 0.063 Q3 0.40 (0.30–0.54) < 0.001 0.44 (0.33–0.59) < 0.001 0.56 (0.40–0.79) < 0.001 Q4 0.33 (0.24–0.45) < 0.001 0.36 (0.26–0.49) < 0.001 0.47 (0.32–0.67) < 0.001 p for trend < 0.001 < 0.001 < 0.001 Per SD 0.65 (0.58–0.72) < 0.001 0.67 (0.60–0.74) < 0.001 0.74 (0.65–0.85) < 0.001 Heart disease Change in the eGDR 1 Ref Ref Ref 2 2.11 (1.49–2.97) < 0.001 2.06 (1.46–2.92) < 0.001 2.01 (1.42–2.86) < 0.001 3 2.24 (1.66–3.03) < 0.001 2.07 (1.52–2.82) < 0.001 1.78 (1.23–2.55) 0.002 Cumulative eGDR Q1 Ref Ref Ref Q2 0.74 (0.53–1.02) 0.07 0.78 (0.56–1.08) 0.138 0.89 (0.62–1.29) 0.546 Q3 0.47 (0.32–0.68) < 0.001 0.51 (0.35–0.74) < 0.001 0.60 (0.39–0.91) 0.018 Q4 0.43 (0.29–0.62) < 0.001 0.47 (0.32–0.68) < 0.001 0.56 (0.36–0.86) 0.009 p for trend < 0.001 < 0.001 0.002 Per SD 0.71 (0.63–0.81) < 0.001 0.74 (0.65–0.84) < 0.001 0.79 (0.67–0.92) 0.002 Stroke Change in the eGDR 1 Ref Ref Ref 2 1.94 (1.24–3.01) 0.003 1.81 (1.16–2.81) 0.009 1.67 (1.06–2.61) 0.025 3 3.16 (2.22–4.56) < 0.001 2.89 (2.02–4.19) < 0.001 1.95 (1.26–3.02) 0.003 Cumulative eGDR Q1 Ref Ref Ref Q2 0.57 (0.39–0.82) 0.003 0.57 (0.39–0.83) 0.003 0.75 (0.49–1.15) 0.186 Q3 0.35 (0.23–0.54) < 0.001 0.38 (0.24–0.59) < 0.001 0.55 (0.33–0.91) 0.022 Q4 0.24 (0.15–0.39) < 0.001 0.26 (0.16–0.42) < 0.001 0.40 (0.22–0.69) 0.001 P for trend < 0.001 < 0.001 < 0.001 Per SD 0.59 (0.51–0.69) < 0.001 0.61 (0.52–0.71) < 0.001 0.73 (0.60–0.88) < 0.001 Crude model, unadjusted for covariates; Model 1, adjusted for age and gender; Model 2, adjusted for age, gender, hukou status, education status, marital status,smoking status, drinking status, TC, LDL-c, eGFR,diabetes, lipid-lowering therapy, antihypertensive therapy, and diabetes treatment. eGDR, estimated glucose disposal rate; CVD, cardiovascular diseases; Q1, Quartile 1; Q2, Quartile 2; Q3, Quartile 3; Q4, Quartile 4; OR, odds ratio; CI, confidence interval; SD, standard deviation. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1TableS1.docx Additionalfile2TableS2.docx Additionalfile3TableS3.docx Additionalfile4TableS4.docx Additionalfile4TableS5.docx Additionalfile4TableS6.docx Cite Share Download PDF Status: Published Journal Publication published 26 Nov, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 23 Sep, 2025 Reviews received at journal 22 Sep, 2025 Reviews received at journal 15 Sep, 2025 Reviewers agreed at journal 09 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers invited by journal 03 Sep, 2025 Editor invited by journal 21 Aug, 2025 Editor assigned by journal 20 Aug, 2025 Submission checks completed at journal 20 Aug, 2025 First submitted to journal 09 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7331938","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512376735,"identity":"0f22f415-6546-4ec8-b327-31bf96e182f3","order_by":0,"name":"Jia Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIie3QMUsDMRTA8Rcjl+XBra8I7Vd4Z+C4QehXueDgIiJ0uUFKINC1H8Did3BxvhJw8gN0bOzq5tLRHJ1cLh0F8w8EAu9HSAByuT+YssICPBKDlNt929F0liLYD4QjUcUt7z8aXdkkGXaOq8R6EladgT5FlF99Ize6lFiTeaFWWBk+d2MEjXtGpnriikVj3uhBQaH1/QiZg3Eykhv28nUXyUJYLK7GCJbhROY+foLZkLF9itDplprl5XWcP4sEJzZMmnz85PaddOUSb8Hy7gBf3bJar/02HJ+W05ly4TBGhi7w11EmxofE8YyhXC6X+8f9AMTlQx7u4VjoAAAAAElFTkSuQmCC","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jia","middleName":"","lastName":"Liu","suffix":""},{"id":512376736,"identity":"57928d94-f98c-4f29-b322-684e4db7b60d","order_by":1,"name":"Wendong Xu","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wendong","middleName":"","lastName":"Xu","suffix":""},{"id":512376737,"identity":"76c92c39-b9e6-49ba-86c8-19b584191ec1","order_by":2,"name":"Yue Zhang","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Zhang","suffix":""},{"id":512376738,"identity":"d222c1f9-da1d-46c7-98b6-e163c8d27229","order_by":3,"name":"Yuan Jia","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Jia","suffix":""},{"id":512376739,"identity":"0f8b3958-97f1-4cf2-b096-85359000ab60","order_by":4,"name":"Shiru Bai","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shiru","middleName":"","lastName":"Bai","suffix":""},{"id":512376740,"identity":"0465fc04-5140-4f6e-bc90-01052ac87b37","order_by":5,"name":"Lu Er","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Er","suffix":""},{"id":512376741,"identity":"24d91f7b-f23a-4659-8385-ac57620fdc5b","order_by":6,"name":"Rongpin Du","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rongpin","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2025-08-09 07:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7331938/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7331938/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-025-05300-8","type":"published","date":"2025-11-26T15:57:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91073264,"identity":"67da5038-4c4d-4950-a3b2-b77c86ea16a3","added_by":"auto","created_at":"2025-09-11 10:57:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39099,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7331938/v1/a2e3ae13b925edea1e8efe14.png"},{"id":91073266,"identity":"4fce4ae5-8492-4414-ba38-2bf0eea19ad7","added_by":"auto","created_at":"2025-09-11 10:57:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10835,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7331938/v1/160d1342819a47719dca7e81.png"},{"id":91075124,"identity":"4fb5b6df-14e7-43a2-b74c-03aba2dc0ff9","added_by":"auto","created_at":"2025-09-11 11:05:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":16102,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7331938/v1/a742a04430db303c18094545.png"},{"id":91073273,"identity":"7c30e35b-fe1e-4074-ac3b-a1de09a1499e","added_by":"auto","created_at":"2025-09-11 10:57:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":12425,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7331938/v1/3e104759d5380b91b2ee95ce.png"},{"id":91073268,"identity":"687bdadf-9b9c-48bd-8ec1-dfa1395132f7","added_by":"auto","created_at":"2025-09-11 10:57:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":48364,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7331938/v1/882c4b0023e7441880a15e0e.png"},{"id":91073275,"identity":"0c9c1735-0e2c-4624-998b-55ca3cb096d8","added_by":"auto","created_at":"2025-09-11 10:57:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":54182,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7331938/v1/675b4991db6995326273f6a7.png"},{"id":97178233,"identity":"40198458-022d-40fc-a607-539c771216dc","added_by":"auto","created_at":"2025-12-01 16:03:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1232462,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7331938/v1/30793833-1e8f-4c17-bfc3-ff3775499d38.pdf"},{"id":91073265,"identity":"665cfbd0-1a09-4bda-ab28-2f98dcc0f090","added_by":"auto","created_at":"2025-09-11 10:57:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14825,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7331938/v1/ec4ce37652d4be17c725d59b.docx"},{"id":91075123,"identity":"816bb76f-1543-4dfe-afd9-b41524d1728c","added_by":"auto","created_at":"2025-09-11 11:05:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14121,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7331938/v1/f87c5cda849b01b6f030eab3.docx"},{"id":91073270,"identity":"761b8660-9d70-4619-9c7e-0c8821bbd520","added_by":"auto","created_at":"2025-09-11 10:57:44","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":36130,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3TableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7331938/v1/7e735c05eecd65c495759931.docx"},{"id":91075127,"identity":"934af6ca-6417-4029-9fa9-fc7e84e8e779","added_by":"auto","created_at":"2025-09-11 11:05:44","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14931,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4TableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7331938/v1/145ce5f0791e11f381f026d7.docx"},{"id":91075126,"identity":"c8e358eb-8028-4180-a3d9-ca4c5ffb6a06","added_by":"auto","created_at":"2025-09-11 11:05:44","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":14706,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4TableS5.docx","url":"https://assets-eu.researchsquare.com/files/rs-7331938/v1/cd2eaf0d1bac50d11dd1c82c.docx"},{"id":91073271,"identity":"42fed760-3d7a-4002-bd88-73f4bec2c248","added_by":"auto","created_at":"2025-09-11 10:57:44","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":14728,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4TableS6.docx","url":"https://assets-eu.researchsquare.com/files/rs-7331938/v1/f17fb240cacbca5bdf4108cf.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Changes in the Estimated Glucose Disposal Rate and Incident Cardiovascular Disease in Patients with Cardiovascular–Kidney–Metabolic Syndrome Stages 0–3: A Prospective Cohort Study in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular\u0026ndash;kidney\u0026ndash;metabolic syndrome (CKM) was formally defined by the American Heart Association (AHA) in 2023.It is a systemic disease characterized by the pathophysiological interactions among obesity, diabetes, chronic kidney disease (CKD), and cardiovascular disease[1]. This pathophysiological interaction significantly increases the risk of serious adverse cardiac, renal, and metabolic events[2], leading to high incidences of morbidity, mortality, and health care burdens worldwide.Approximately 90% of adults worldwide have stage 1 or higher CKM syndrome[1,3]. This widespread distribution of a high-risk population means that effective early intervention strategies have significant public health value.In China, this challenge is particularly acute, where CVD accounts for more than 45% of all-cause mortality, and the clinical burden of CKM syndrome is driven mainly by CVD[4]. Individuals with CKM syndrome stages 0\u0026ndash;3 are often asymptomatic but require focused prevention of CVD events; however, traditional models often fail to comprehensively integrate metabolic, renal, and vascular biomarkers, which exacerbates the difficulty of early identification and precise risk stratification[1,5].\u003c/p\u003e\n\u003cp\u003eInsulin resistance is a core driver of CKM syndrome progression[6],\u0026nbsp;exacerbating metabolic dysregulation[7], endothelial damage[8],\u0026nbsp;and organ fibrosis[9,10]. The hyperinsulinemic\u0026ndash;euglycemic clamp (HIEC) technique is the gold standard for detecting IR;however, the invasiveness, complexity, time-consuming nature, and high cost of this technique make it impractical for routine clinical practice and widespread application[11]. Homeostasis model assessment of insulin resistance (HOMA-IR), a surrogate marker for IR, is calculated on the basis of fasting insulin values, which are not commonly tested in routine clinical practice[12]. Therefore, there is an urgent need to find alternative markers of IR that are easily accessible and applicable in routine clinical practice. Recently, the estimated glucose disposal rate (eGDR), which is calculated from readily available clinical parameters, including hemoglobin A1c (HbA1c), hypertension, and waist circumference (WC), has emerged as a more convenient surrogate marker for IR[13]\u0026nbsp;and is more suitable for large-scale application and routine clinical practice. Compared with HOMA-IR, eGDR performs better in predicting cardiovascular risk, all-cause mortality, and cardiovascular mortality[14\u0026ndash;16]. A higher eGDR indicates lower level of IR, whereas a lower eGDR indicates higher level of IR. eGDR has been shown in a recent study to identify those at high risk of CVD in patients with CKM stages 0\u0026ndash;3[4,17].\u003c/p\u003e\n\u003cp\u003eHowever, a single eGDR measurement reflects a static IR status and fails to capture the dynamic changes in IR over time and their potential impact. Given the central driving role and possible time-varying nature of IR in CKM syndrome, systematically evaluating the relationship between the longitudinal trajectory of eGDR change and CVD risk is crucial for achieving more precise dynamic risk stratification and disease management.Unfortunately, there is an extreme paucity of research in this area.\u003c/p\u003e\n\u003cp\u003eTherefore, the aim of this study was to utilize prospective cohort data from the CHARLS to explore the associations between the dynamic trajectory of eGDR after baseline and the risk of new-onset CVD in Chinese adult patients with CKM syndrome. In addition, to assess the impact of long-term IR exposure, we also examined the association between cumulative eGDR level and the risk of new-onset CVD in Chinese patients with CKM syndrome.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e1. \u0026nbsp;Data source and study population\u003c/p\u003e\n\u003cp\u003eThe data used in this study were derived from the China Health and Retirement Longitudinal Study, which is a nationally representative longitudinal cohort designed to investigate the health, socioeconomic, and behavioral characteristics of middle-aged and elderly individuals (\u0026ge;45 years) in China. The CHARLS baseline survey was initiated in 2011 (Wave 1), covering 150 counties/districts and 450 villages/communities in 28 provinces across the country and utilizing a stratified multistage probability sampling methodology with a total of 17,708 respondents from 10,257 households, representing the diversity of the middle-aged and elderly population in China. Follow-up surveys were conducted every 2\u0026ndash;3 years, including those in 2013 (Wave 2), 2015 (Wave 3), 2018 (Wave 4), and 2020 (Wave 5). In addition to questionnaires, CHARLS collected venous blood samples from participants in Wave 1 and Wave 3 to measure biomarkers, including glucose, lipids, and other biomarkers, which provide important data to support the study of metabolic diseases. Detailed information on sampling methods, anthropometric measurements and blood biomarker information for CHARLS has been previously documented in previous publications.\u003c/p\u003e\n\u003cp\u003eThe datasets from Wave 1 (baseline, 2011) and Wave 3 (follow-up, 2015) were extracted, considering the available blood test data. Each participant was required to fast overnight and their blood was collected and analyzed at the center by medical personnel from the Chinese Center for Disease Control and Prevention according to standard protocols. All research laboratories were standardized and accredited. The fasting blood glucose (FBG) concentration was measured via the enzyme colorimetric method, and hemoglobin A1c (HbA1c) was evaluated with boric acid affinity high-performance liquid chromatography(HPLC)[18].\u003c/p\u003e\n\u003cp\u003eWe first included 11,847 participants who completed blood tests in Wave 1 (2011), as\u0026nbsp;blood samples were only collected in Waves 1 and 3. A total of 2,862 participants were included in the final study after meeting the following exclusion criteria: (1) inability to define the CKM syndrome stage in Wave 1, (2) lack of CVD status in subsequent follow-up, (3) inability to define eGDR in both Wave 1 and Wave 3, (4) lack of demographic information, and (5) preexisting CVD before 2015. Figure 1 presents the study population selection process.\u003c/p\u003e\n\u003cp\u003eThe CHARLS study was approved by the Ethics Review Board of Peking University Biomedical (IRB00001052\u0026ndash;11015). All participants signed written informed consent forms before the start of the study. The principles outlined in the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) were followed in the current study[19]. Detailed information on the CHARLS data is accessible on its official website (http://charls.pku.edu.cn/en)[18].\u003c/p\u003e\n\u003cp\u003eData assessment and definitions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAssessment of exposure\u003c/p\u003e\n\u003cp\u003eThe eGDR was computed utilizing the following formula:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eeGDR = 21.158 \u0026minus; (0.09 \u0026times; WC) \u0026minus; (3.407 \u0026times; HT) \u0026minus; (0.551 \u0026times; HbA1c)[13], where WC is waist circumference (cm) and HT is hypertension status (yes = 1, no = 0)\u003c/p\u003e\n\u003cp\u003eThe cumulative of eGDR was calculated as follows: CumeGDR=[(eGDR\u003csub\u003e2012\u003c/sub\u003e+eGDR\u003csub\u003e2015\u003c/sub\u003e)/2]*time(2015-2012)\u003c/p\u003e\n\u003cp\u003eAssessment of outcomes\u003c/p\u003e\n\u003cp\u003eThe outcome of this study was the incidence of CVD, including heart disease and stroke. Consistent with previous studies[20\u0026ndash;22], incident CVD events were assessed by standardized questions:\u0026quot;Have you been diagnosed by a doctor as having a stroke/heart disease (including a heart attack, coronary heart disease, angina pectoris, congestive heart failure, or other heart problems)?\u0026quot; or \u0026quot;Are you currently receiving any of the following treatments (taking traditional Chinese medicines/taking modern Western medicines/other treatments/none of the above) for stroke/heart disease or its complications?\u0026quot;. Participants who reported \u0026quot;yes\u0026quot; to a physician diagnosis of heart disease or stroke or indicated receiving specific treatment for heart disease or stroke were defined as having cardiovascular disease.\u003c/p\u003e\n\u003cp\u003eDefinition of CKM syndrome stages 0 to 4\u003c/p\u003e\n\u003cp\u003eThe stages of CKM syndrome from 0 to 4 are classified in accordance with the AHA Presidential Advisory Statement[1]. The CKM syndrome stages 0\u0026ndash;4 are specified as follows: stage 0: no CKM syndrome health risk factors; stage 1: abdominal obesity and/or prediabetes; stage 2: metabolic disorders(type 2 diabetes, hypertension, and high triglycerides) or renal disorders; stage 3: subclinical CVD in the context of CKM syndrome; and stage 4: clinical CVD (coronary heart disease, heart failure, stroke, peripheral artery disease, atrial fibrillation) in the context of CKM syndrome. Subclinical CVD is defined as having a \u0026ge;20% 10-year CVD risk or high-risk CKD according to the American Heart Association (AHA) Predicting Risk of CVD Events (PREVENT) equations.\u003c/p\u003e\n\u003cp\u003eData collection\u003c/p\u003e\n\u003cp\u003eThis study collected the following data: (1) demographic characteristics: gender, age, educational status (primary school or below, secondary school, college or above), hukou status (urban/rural), and marital status (married or other); (2) lifestyle factors: smoking status (never, former, current) and drinking status (never, former, current); (3) physical measurements: systolic blood pressure (SBP), diastolic blood pressure (DBP), and body mass index (BMI, kg/m\u0026sup2;); (4) medical history: self-reported physician-diagnosed hypertension, diabetes, dyslipidemia, kidney disease, liver disease, lipid-lowering therapy, antihypertensive therapy, and diabetes treatment. Hypertension was defined as an average SBP \u0026ge;140 mmHg, or an average DBP \u0026ge;90 mmHg at baseline, or current use of antihypertensive medication, or self-reported history of hypertension[23]. Diabetes was defined as a fasting blood glucose level \u0026ge;126 mg/dl (7 mmol/L) and/or a random blood glucose level \u0026ge;200 mg/dl (11.1 mmol/L) and/or an HbA1c level \u0026ge;6.5% at baseline and/or self-reported history of diabetes or current use of anti-diabetic medication[24]. Other medical conditions were ascertained on the basis of self-reported history or receipt of any condition-specific treatment. (5) Laboratory examination: serum creatinine (Scr), blood urea nitrogen (BUN), uric acid (UA), estimated glomerular filtration rate (eGFR), fasting blood glucose (FBG), HbA1c, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) level. The eGFR was determined using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation[25].\u003c/p\u003e\n\u003cp\u003eHandling of missing variables\u003c/p\u003e\n\u003cp\u003eAdditional file 1: Table S1 illustrates the distribution of missing data for the included study participants. To maximize the sample size, we used multiple interpolation with chained equations (MICE) and implemented this multiple interpolation process through the \u0026ldquo;MICE\u0026rdquo; package, despite the small proportion of missing data.\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eNormally distributed continuous variables are expressed as the means \u0026plusmn; standard deviations (SDs), whereas nonnormally distributed variables are presented as the medians with interquartile ranges (IQRs). Categorical variables are reported as frequencies with percentages (%). Group comparisons were performed with \u0026chi;\u0026sup2; tests for categorical variables, ANOVA for normally distributed continuous variables, and the Kruskal‒Wallis test for nonparametric data.\u003c/p\u003e\n\u003cp\u003eFirst, participants were classified into three groups using K-means clustering on the basis of their eGDR values from 2012 to 2015. K-means clustering is a technique that aims to divide N observations into K clusters, and K-means clustering effectively captures subtle changes in the variation in the dynamic eGDR and uses the elbow method to determine the optimal number of clusters[26]. Once the best-fit model was determined, participants were assigned to their most likely class. However, this approach is partly subjective and relies on the visual interpretation of the \u0026ldquo;elbow\u0026rdquo; . As shown in Figure 2A, the rate of reduction in within-cluster variance (distortion) declined significantly beyond K = 3, indicating an inflection point. At this threshold, adding more clusters provided negligible explanatory power (the \u0026lsquo;elbow\u0026rsquo; criterion). Accordingly, as shown in Figure 2B, the participants were stratified into three classes. Class 1 was characterized by persistently high eGDR level, with eGDR ranging from 10.96\u0026plusmn;1.38 in 2012 to 10.59\u0026plusmn;0.81 in 2015, indicating optimal eGDR control. Class 2 was characterized by initially high eGDR that significantly decreased to low level, with eGDR ranging from 10.18\u0026plusmn;1.28 in 2012 to 7.27\u0026plusmn;1.11 in 2015, suggesting impaired eGDR control. Class 3 was characterized by persistently low eGDR level, with eGDR ranging from 6.59\u0026plusmn;0.99 in 2012 to 6.26\u0026plusmn;1.36 in 2015, indicating poor eGDR control. Additionally, we used continuous cumeGDR and quartiles of the cumeGDR group to assess the associations between long-term changes in the eGDR and CVD incidence.\u003c/p\u003e\n\u003cp\u003eLogistic regression analyses were conducted to evaluate the associations between cumeGDR, eGDR control level and CVD events, with odds ratios (ORs) and 95% confidence intervals (95% CIs) calculated across three models. The crude model was unadjusted for covariates. Model 1 was adjusted for age and gender. Model 2 included adjustments for all covariates included in Model 1 and in addition was adjusted for hukou status, education status, marital status, smoking status, drinking status, TC, LDL, eGFR, diabetes, lipid-lowering therapy, antihypertensive therapy, and diabetes treatment. Multicollinearity was tested using the variance inflation factor (VIF) method, with a VIF \u0026ge; 5 indicating the presence of multicollinearity. Additional file 2: Table S2 indicating evidence of no significant multicollinearity. Furthermore, restricted cubic spline (RCS) regression analysis was employed to examine linear and dose‒response relationships between cumeGDR and CVD incidence in participants with CKM syndrome. Additionally, various subgroup and interaction analyses were performed to detect potential effect modifications. The participants were stratified into subgroups by age (\u0026lt;60 years vs. \u0026ge;60 years), gender (male vs. female), marital status (married vs. other), hukou status (urban/rural), and education status (\u0026lt;middle school vs. \u0026ge;middle school). Sensitivity analysis were performed to assess the robustness of the primary results. First, we applied a logistic regression model that excluded participants with missing values for covariates to mitigate the potential influence of missing values on the primary results. Second, the data were reanalyzed after excluding participants with cancer at baseline (2015) to assess the potential impact of preexisting cancer on the observed associations. Finally, we extended the follow-up of these participants until 2020 to test the stability of the results over a longer time period.\u003c/p\u003e\n\u003cp\u003eAll the statistical analyses were performed using R software version 4.3.1 (http://www.R-project.org/), and a two-sided \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e1.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Population characteristics\u003c/p\u003e\n\u003cp\u003eFigure 1 illustrates the inclusion and exclusion criteria used in this study. Specifically, we included 17,708 participants in Wave 1 of the CHARLS cohort. Of these, 10,455 participants were unable to be classified into CKM syndrome stage in Wave 1, 232 participants were excluded because of incomplete follow-up data, 286 participants were excluded because they had CVD at baseline, 3,870 participants were excluded because of missing eGDR data in Waves 1 and 3, and 3 participants were excluded because of a lack of demographic data.\u0026nbsp;A total of 2,862 participants with a mean age of 57 years (51\u0026ndash;63 years) were included in the final study, of which 1,563 (54.6%) were female. The average eGDR of the participants was 9.42 in 2012 and 8.56 in 2015. The cumeGDR for the entire cohort was 28.22. Table 1 summarizes the baseline characteristics of participants with CKM syndrome stages 0\u0026ndash;3 in the three groups on the basis of the level of eGDR control (Classes 1\u0026ndash;3). These classes were significantly different in terms of age, gender, education level, smoking, drinking, CKM syndrome stage, BMI, SBP, DBP, comorbidities (hypertension, diabetes, dyslipidemia), medication status (antihypertensive drugs, hypoglycemic drugs, lipid-lowering drugs), and biochemical markers (HbA1c, FBG, TG, TC, HDL-C, LDL-C, UA, eGFR). Specifically, Class 3 represents the most severely metabolically dysregulated patients, with an older age, higher BMI and blood pressure, more comorbidities, and more severe biochemical abnormalities than the other groups. Table 1 summarizes the baseline characteristics of the participants with stages 0\u0026ndash;3 CKM syndrome in the 3 groups on the basis of the level of eGDR control (Classes 1\u0026ndash;3). In addition, baseline characteristics stratified by cumeGDR quartiles are detailed in Table S3.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Associations between eGDR changes and new-onset CVD\u003c/p\u003e\n\u003cp\u003eDuring the 3-year follow-up (from Wave 3 in 2015 to Wave 4 in 2018), 404 participants (14.1%) had CVD events, namely, 254 had heart attacks (8.9%) and 177 had strokes (6.2%). Logistic analysis was applied to study the relationship between eGDR changes and new-onset CVD. As shown in Table 2, different IR control states reflected by eGDR were observed after individuals were grouped via K-means clustering analysis. Compared with Class 1, Classes 2 and 3 had a significantly greater risk of new-onset CVD (Class 2: OR 1.82, 95% CI 1.36\u0026ndash;2.45, P\u0026lt;0.001; Class 3: OR 1.90, 95% CI 1.41\u0026ndash;2.56, P\u0026lt;0.001), heart disease (Class 2: OR 2.01, 95% CI 1.42\u0026ndash;2.86, P\u0026lt;0.001; Class 3: OR 1.78, 95% CI 1.23\u0026ndash;2.55), P=0.002, and stroke (Class 2: OR 1.67, 95% CI 1.06\u0026ndash;2.61, P=0.025; Class 3: OR 1.95, 95% CI 1.26\u0026ndash;3.02, P=0.003). Cumulative eGDR was also used to assess eGDR changes. When the first quartile (the group with the lowest cumulative level) was used as a reference, with increasing cumeGDR, the event rates of new CVD, heart disease, and stroke gradually decreased (p for trend \u0026lt;0.001). These results remained consistent after correction for potential confounders.\u003c/p\u003e\n\u003cp\u003eRestricted cubic spline (RCS) models were applied to assess the dose‒response relationships between cumeGDR and new-onset CVD, heart disease, and stroke. As shown in Figure 3, the fully adjusted RCS model revealed a negative linear correlation between cumeGDR and new-onset CVD (P for overall\u0026lt; 0.001, P for nonlinearity= 0.922). In addition, as shown in Figure4, the RCS model revealed a negative linear correlation between cumeGDR and incident heart disease and stroke (heart disease: P for overall = 0.021, P for nonlinearity= 0.825; stroke: P for overall = 0.007, P for nonlinearity= 0.565).\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Subgroup analysis\u003c/p\u003e\n\u003cp\u003eTo further investigate the relationships between eGDR changes and cumeGDR and new-onset CVD, a series of subgroup analyses were conducted. As shown in Figures 5 and 6, subgroups stratified by age, gender, hukou status, education, and marital status did not influence the relationships between eGDR changes and cumeGDR and new-onset CVD (all P\u0026gt;0.05 for interaction).\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sensitivity analysis\u003c/p\u003e\n\u003cp\u003eSensitivity analyses confirmed the robustness of the primary results. First, after removing participants with missing covariates in the fully adjusted model, the association between the control level of eGDR and the incidence of CVD remained consistent with our study results (Class 2: OR 1.82, 95% CI 1.38\u0026ndash;2.49, P\u0026lt;0.001; Class 3: OR 1.95, 95% CI 1.44\u0026ndash;2.62, P\u0026lt;0.001). Furthermore, a negative correlation persisted between cumulative eGDR and the new-onset of CVD (p for trend \u0026lt;0.001), as detailed in Additional File 4: Table S4. Second, after excluding individuals with cancer at the baseline in 2015, poor eGDR control level was associated with an increased risk of new-onset CVD (Class 2: OR 1.83, 95% CI 1.36\u0026ndash;2.45, P\u0026lt;0.001; Class 3: OR 1.92, 95% CI 1.42\u0026ndash;2.57, P\u0026lt;0.001) and the association between cumeGDR and CVD was consistent with the primary findings (p for trend \u0026lt;0.001) (Additional file 4: Table S5). Finally, we extended the follow-up of these participants until 2020, and consistent with our study, the risk of CVD continued to increase in the group with a significant decrease in eGDR and in the group with persistently low level (Class 2: OR 2.06, 95% CI 1.56\u0026ndash;2.72, P\u0026lt;0.001; Class 3: OR 2.31, 95% CI 1.75\u0026ndash;3.04, P\u0026lt;0.001). There was a negative correlation between cumeGDR and the incidence of CVD (p for trend \u0026lt;0.001)(Additional file 4: Table S6).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we dynamically assessed the longitudinal trajectory of eGDR in patients with CKM syndrome stages 0\u0026ndash;3 by using K-means clustering analysis, revealing a significant association with cardiovascular disease risk. Our study revealed that individuals with a persistently low eGDR had a significantly greater risk of new-onset CVD in this high-risk population with CKM syndrome, and this study further confirmed a significant linear negative correlation between the cumeGDR and the risk of CVD in patients with CKM syndrome stages 0\u0026ndash;3. This finding indicates that eGDR is not just a static marker of IR; its dynamic trajectory and long-term cumeGDR are assessment metrics that better capture the nature of disease progression. Focusing on the CKM syndrome population with disturbed multisystem interactions, this study provides key evidence for the early identification of high-risk individuals and is of significant clinical value in establishing dynamic risk prediction models and realizing precise interventions for high-risk individuals.\u003c/p\u003e\u003cp\u003eIR is characterized by reduced sensitivity to the physiological effects of insulin, which can lead to abnormal glucose and lipid metabolism, directly drive atherosclerosis, and induce endothelial dysfunction, chronic inflammation, and oxidative stress, accelerating arteriosclerosis and thrombosis[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition, IR is significantly associated with the risk of cardiovascular disease, chronic kidney disease, and metabolic disorders[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Particularly in the CKM syndrome population, IR drives cardiac, renal, and metabolic damage simultaneously, creating a vicious cycle[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] in which all three diseases often co-occur, and having more than one of these diseases at the same time exponentially increases the risk of death, primarily due to cardiovascular disease[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]; thus, early intervention reduces the risk of progression, and attention needs to be paid to evaluating IR. Although the hyperinsulinemic\u0026ndash;euglycemic clamp (HIEG) and HOMA-IR are considered reliable metrics for assessing IR, their complex measurement techniques limit their practical application[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The triglyceride‒glucose(TyG) index does not integrate clinical parameters (e.g., blood pressure, obesity), and its predictive stability is insufficient. To overcome the limitations of the above measurements, the eGDR was developed by the Pittsburgh Epidemiology of Diabetes Complications (EDC) study and further validated in type 1 diabetes[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The eGDR integrates metabolic, obesity, and glycemic control parameters and is superior to a single indicator, with a correlation of r\u0026thinsp;=\u0026thinsp;0.63 with the HIEG clamp[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and its predictive efficacy exceeds that of six alternative IR indicators (TyG index, TyG-waist circumference, TyG-body mass index, TyG-waist-to-height ratio, triglyceride-to-high density lipoprotein cholesterol ratio, and metabolic score for insulin resistance)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. On the basis of these findings, eGDR seems to be a reasonable alternative indicator for IR estimation. The eGDR has been proven to effectively predict CVD events and stroke risk in different populations, including those with diabetes, those without diabetes, and the general population[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Notably, in the CKM syndrome population, each 1-unit increase in eGDR was associated with a 9% reduction in the risk of CVD (HR 0.91, 95% CI 0.88\u0026ndash;0.93)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Given that patients in the early stages of CKM syndrome (stages 0\u0026ndash;3) generally have visceral obesity and chronic inflammation and that IR is the core driving factor, this study focused on this population. Previous studies have focused mostly on single-point measurements of baseline eGDR and have not considered the impact of eGDR fluctuations during follow-up. In contrast, the progression of CKM syndrome essentially reflects the continuous drift of metabolic parameters rather than a simple stage transition. Thus, capturing dynamic changes in the eGDR may be more capable of reflecting disease progression and risk than single-point measurements are. We applied K-means clustering to analyze the dynamic changes in eGDR and compared with the static limitation of the traditional quartile method. This method was used to identify three types of clinical phenotypes: persistently high, significantly decreased, and persistently low. A recent study in the general population revealed that participants with consistently low eGDR were associated with a significantly greater risk of CVD risk[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], confirming the importance of dynamic assessment. By identifying specific eGDR changes (such as persistently high, significantly decreased, and persistently low), it is possible to identify high-risk individuals more accurately, optimize risk stratification, and reflect the effectiveness of interventions through dynamic monitoring. For example, treatment with glucose-lowering drugs (such as glucagon-like peptide-1 receptor agonist, GLP-1RA) improves insulin sensitivity[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Previous studies have mostly assessed β-cell function by HOMA-IR, and in the future, we can use acquired metrics, such as eGDR, to provide a quantitative bridge for drug efficacy assessment.\u003c/p\u003e\u003cp\u003e Our study used K-means clustering analysis to divide CKM syndrome participants into three subgroups on the basis of their eGDR from 2012 to 2015. The results revealed that the eGDR in all subgroups tended to decrease, indicating that the degree of IR increased with age, which was consistent with the findings of previous studies[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and which may be attributed to physiological decline, increased adipose tissue, decreased skeletal muscle, and a lack of effective interventions. Compared with Class 1 (persistently high group), the risk of new-onset CVD was significantly greater in Class 2 (significantly decreased group) and Class 3 (persistently low group). When further classified on the basis of the quartiles of cumeGDR, participants with the lowest cumeGDR had the highest risk of new-onset CVD. After adjusting for potential confounding factors, the above associations remained robust. The RCS curve revealed a negative linear relationship between cumeGDR and new-onset CVD. These findings indicate that eGDR level are independently associated with new-onset CVD.\u003c/p\u003e\u003cp\u003eFrom a mechanistic perspective, the eGDR, as an alternative marker for IR, not only reflects reduced insulin sensitivity but also captures multidimensional dysregulation, including glucose and lipid metabolism disorders, endothelial dysfunction, chronic inflammation, and oxidative stress[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These interrelated pathological processes collectively contribute to the onset and progression of CVD in patients with CKM syndrome. First, IR increases the production of very low-density lipoprotein (VLDL), and its metabolic product, residual lipoprotein, is deposited in the vascular endothelium, accelerating plaque formation[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].Elevated free fatty acids (FFAs) in the IR state lead to increased ceramide and diacylglycerol(DAG), inhibiting glucose transporter-4 (GLUT-4) membrane translocation in skeletal muscle and adipose tissue, interfering with glucose uptake, and forming a vicious cycle of hyperglycemia and hyperinsulinemia[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The persistent lipotoxic environment leads to abnormal accumulation of FFAs in cardiomyocytes, directly damaging myocardial contractile function by inhibiting mitochondrial β-oxidation and increasing reactive oxygen species (ROS) production[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].Second, the core damage caused by IR to the vascular system lies in disrupting the dual-pathway balance of insulin signaling, leading to endothelial dysfunction[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The PI3K/A-t pathway mediated by insulin for vasodilation is inhibited, leading to decreased endothelial nitric oxide synthase (eNOS) activity and reduced NO production, whereas the MAPK pathway is overly activated, resulting in increased endothelin-1 (ET-1), causing sustained vasoconstriction and endothelial dysfunction[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Notably, this process synergistically exacerbates endothelial damage with lipotoxicity. A reduction in NO weakens the antioxidant capacity of blood vessels, whereas FFA deposition directly damages the endothelium. The combined effects of these two factors lay an important pathological foundation for atherosclerosis[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Finally, recent studies have shown that IR and chronic inflammation have a bidirectional promoting relationship, accelerating the progression of CKM syndrome[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. IR triggers the release of FFAs and proinflammatory adipokines (such as leptin and resistin) from adipose tissue, activates monocyte differentiation into M1 macrophages, and upregulates the expression of proinflammatory factors such as TNF-α, IL-6, and IL-12[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This systemic chronic inflammation not only directly damages the vascular endothelium but also further inhibits tyrosine phosphorylation of insulin receptor substrate (IRS) by activating the classic IKKβ-NF-κB molecular pathway, exacerbating insulin resistance[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Moreover, a hyperglycemic environment promotes the accumulation of advanced glycation end products (AGEs)[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and activates NADPH oxidase, leading to excessive production of reactive oxygen species (ROS). ROS play multiple destructive roles in the pathogenesis of CVD. ROS directly oxidize low-density lipoprotein (LDL) to form ox-LDL, promote foam cell formation, and increase plaque instability[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]; they also impair myocardial cell mitochondrial function and reduce energy production efficiency[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. ROS activate the NLRP3 inflammasome, promoting the maturation and release of proinflammatory factors such as IL-1; at the same time, inflammatory cell infiltration generates more ROS. These multiple interrelated mechanisms collectively lead to early atherosclerosis in patients with CKM syndrome.Our findings indicate that dynamic IR monitoring can comprehensively reflect the pathophysiological processes in patients with CKM syndrome and that the cumulative effects of these pathophysiological processes are reflected in the cumeGDR. Persistently low eGDR level indicate long-term exposure to metabolic disorders, chronic inflammation, and oxidative stress, all of which synergistically increase the risk of CVD in patients with CKM syndrome. Therefore, dynamic monitoring of eGDR changes can be used to assess patients' metabolic status comprehensively and is helpful for the early identification of high-risk populations for CVD.\u003c/p\u003e\u003cp\u003eOur study has several advantages, including the use of a large, nationally representative CHARLS cohort and the construction of a dynamic change trajectory of eGDR through K-means clustering, which overcomes the limitations of traditional single measurements and accurately identifies the high-risk population of metabolic deterioration in patients with stages 0\u0026ndash;3 CKM syndrome. However, our study has certain limitations. First, only two blood tests were performed, which prevented a more detailed characterization of eGDR. When eGDR is only evaluated at a specific time, its continuous changes over time are not fully captured. Future studies with more frequent measurements could provide deeper insights into the dynamic nature of metabolic disorders. Second, although we adjusted for multiple confounding factors, residual confounding factors, such as dietary patterns and socioeconomic factors, cannot be completely ruled out and may affect the causal inference between eGDR and CVD. Our findings are particularly applicable to middle-aged and elderly populations in China and do not cover young CKM syndrome patients whose metabolic characteristics and dynamic changes in eGDR may present different patterns. The diagnosis of cardiovascular disease is based on self-reported physician diagnoses rather than imaging (CT or MRI), which introduces potential misclassification bias. However, the reliability of self-reported cardiovascular events has been validated, providing some assurance of reliability. Finally, our study adopted strict exclusion criteria (incomplete CKM syndrome data or missing CVD records), which were consistent with previous studies. Excluding certain participants may not cause bias in the study results.\u003c/p\u003e\u003cp\u003eAs a comprehensive indicator reflecting insulin resistance and metabolic function, the eGDR can be calculated with only routine clinical data. It is low-cost, easily accessible, and suitable for grassroots settings such as community health centers and rural clinics. This study demonstrated that its dynamic changes (such as persistently low level and significant decreases) can significantly increase the risk of CVD, which is particularly applicable to primary medical institutions with limited health care resources and can be used as a tool for the initial screening of high-risk populations. Dynamic changes in eGDR can occur earlier than clinical symptoms can, suggesting deterioration of metabolic function. Early interventions such as lifestyle modification and intensive management of blood glucose/blood pressure in patients with persistently low level or significantly declining level may halt the progression of CVD and avoid the high-cost burden of late-stage treatment. The cumeGDR integrates long-term metabolic status and is more predictive than a single measurement. Its linear association with CVD simplifies risk assessment models. Clinicians can intuitively determine the long-term risk level of patients by periodically calculating the cumeGDR.\u003c/p\u003e\u003cp\u003eBased on the results and limitations of this study, future research could focus on the following directions: (1) Firstly, it is necessary to verify the change validate the generalisability and predictive value of the eGDR change trajectories and cumulative eGDR metrics in a wider range of populations (encompassing different ethnicities and age groups).\u003c/p\u003e\u003cp\u003eBuilding on the findings and limitations of this study, future research could focus on the following directions: (1) First, it is necessary to validate the generalizability and predictive value of eGDR change and cumeGDR in a wider range of populations, such as those of different races and age groups. Moreover, the integration of multiomics data, such as genomics and metabolomics data, to analyze the molecular mechanisms behind the dynamic changes in eGDR will provide key insights into understanding its biological basis. (2) On this basis, a CVD risk prediction model should be developed and validated, integrating eGDR and other relevant factors to improve the identification of high-risk individuals. (3) Ultimately, it is critical to conduct prospective interventional studies to validate whether increasing eGDR levels through specific strategies, such as lifestyle interventions for weight loss or targeted drug therapies, is effective in reducing the risk of subsequent CVD events, thereby providing direct causal evidence for the development of prevention strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePoorly controlled eGDR level significantly increase the risk of new-onset CVD in patients with CKM syndrome stages 0\u0026ndash;3. Dynamic monitoring of eGDR changes can optimize the risk stratification of CVD, and these findings help to more accurately identify high-risk populations and provide key evidence for early intervention to reduce the burden of cardiovascular disease.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCKM Cardiovascular-kidney-metabolic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCVD Cardiovascular diseases\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eeGDR estimated glucose disposal rate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIR Insulin resistance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHARLS China Health and Retirement Longitudinal Study\u003c/p\u003e\n\u003cp\u003eCKD Chronic Kidney disease\u003c/p\u003e\n\u003cp\u003eAHA American Heart Association\u003c/p\u003e\n\u003cp\u003eHIEC hyperinsulinemic-euglycemic clamp\u003c/p\u003e\n\u003cp\u003eHOMA-IR Homeostasis model assessment of insulin resistance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHbA1c Hemoglobin A1c\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWC Waist circumference\u003c/p\u003e\n\u003cp\u003eFBG Fasting blood glucose\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHPLC High performance liquid chromatography\u003c/p\u003e\n\u003cp\u003eSTROBE Strengthening the Reporting of Observational Studies in Epidemiology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHT Hypertension\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePREVENT Predicting Risk of CVD Events\u003c/p\u003e\n\u003cp\u003eSBP Systolic blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDBP Diastolic blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI Body mass index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eScr Serum creatinine\u003c/p\u003e\n\u003cp\u003eBUN Blood urea nitrogen\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUA Uric acid\u003c/p\u003e\n\u003cp\u003eTC Total cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTG Triglyceride\u0026nbsp;\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\u003eCKD-EPI Chronic Kidney Disease Epidemiology Collaboration\u003c/p\u003e\n\u003cp\u003eMICE Multiple interpolation with chained equations\u003c/p\u003e\n\u003cp\u003eSD Standard deviation\u003c/p\u003e\n\u003cp\u003eIQR Interquartile range\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOR Odds ratios\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI Confidence interval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQ Quartiles\u003c/p\u003e\n\u003cp\u003eVIF Variance inflation factor\u003c/p\u003e\n\u003cp\u003eRCS Restricted cubic spline\u003c/p\u003e\n\u003cp\u003eHR Hazard ratio\u003c/p\u003e\n\u003cp\u003eTyG Triglyceride-glucose\u003c/p\u003e\n\u003cp\u003eGLP-1RA Glucagon-like peptide-1 receptor agonist\u003c/p\u003e\n\u003cp\u003eVLDL Very low-density lipoprotein\u003c/p\u003e\n\u003cp\u003eFFAs Free fatty acids\u003c/p\u003e\n\u003cp\u003eDAG Diacylglycerol\u003c/p\u003e\n\u003cp\u003eGLUT-4 Glucose transporter-4\u003c/p\u003e\n\u003cp\u003eROS \u0026nbsp;Reactive oxygen species\u003c/p\u003e\n\u003cp\u003eKDIGO Kidney Disease Improvement Global Outcomes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePI3K-Akt Phosphatidylinositol kinase/protein kinase B\u003c/p\u003e\n\u003cp\u003eeNOS endothelial nitric oxide synthase\u003c/p\u003e\n\u003cp\u003eMAPK Mitogen-activated protein kinase\u003c/p\u003e\n\u003cp\u003eET-1 Endothelin-1\u003c/p\u003e\n\u003cp\u003eTNF-\u0026alpha;Tumor Necrosis Factor-\u0026alpha;\u003c/p\u003e\n\u003cp\u003eIL Interleukin\u003c/p\u003e\n\u003cp\u003eIRS Insulin receptor substrate\u003c/p\u003e\n\u003cp\u003eNF-\u0026kappa;B Nuclear factor-kappa B\u003c/p\u003e\n\u003cp\u003eAGEs Advanced glycation end products\u003c/p\u003e\n\u003cp\u003eNADPH Nicotinamide adenine dinucleotide phosphate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNLRP NOD-like receptor thermal protein domain assiciated protein\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethics review committee (institutional review board) of Peking University. All participants provided official written consent for their participation.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during this study are available in the China Health and Retirement Longitudinal Study repository [http:// charls.pku.edu.cn].\u003c/p\u003e\n\u003cp\u003eCompeting interests \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAuthor contributions \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJL, RPD, and WDX conceived and designed the study. JL, YZ, LE and YJ conducted the research. JL and SRB analyzed the data. JL wrote the manuscript. All authors read and approved of the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe author thanks all CHALRS members for their contributions, as well as the participants who provided data. We also thank AJE academic editorial team (https://www.aje.cn/) for language editing service.\u003c/p\u003e\n\u003cp\u003eAuthor details \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Cardiology, Hebei General Hospital, NO.348 Heping West Road, Shijiazhuang, Hebei, 050000, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNdumele CE, Neeland IJ, Tuttle KR, Chow SL, Mathew RO, Khan SS, et al. A Synopsis of the Evidence for the Science and Clinical Management of Cardiovascular-Kidney-Metabolic (CKM) Syndrome: A Scientific Statement From the American Heart Association. Circulation. 2023;148:1636\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarassi M, Fadini GP. The cardio-renal-metabolic connection: a review of the evidence. Cardiovasc Diabetol. 2023;22:195.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu L, Chen Y, Liu B, Li X, Wang J, Nie Z, et al. Association between cumulative changes of the triglyceride glucose index and incidence of stroke in a population with cardiovascular-kidney-metabolic syndrome stage 0\u0026ndash;3: a nationwide prospective cohort study. Cardiovasc Diabetol. 2025;24:202.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTan Z, Zhou D, Tang Y, Huo G. Association between estimated glucose disposal rate and incident cardiovascular disease in a population with Cardiovascular-Kidney-Metabolic syndrome stages 0\u0026ndash;3: insights from CHARLS. Front Cardiovasc Med. 2025;12:1537774.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTao S, Yu L, Li J, Wu J, Huang X, Xie Z, et al. Insulin resistance quantified by estimated glucose disposal rate predicts cardiovascular disease incidence: a nationwide prospective cohort study. Cardiovasc Diabetol. 2025;24:161.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDing X, Tian J, Chang X, Liu J, Wang G. Association between remnant cholesterol and the risk of cardiovascular-kidney-metabolic syndrome progression: insights from the China Health and Retirement Longitudinal Study. Eur J Prev Cardiol. 2025;zwaf248.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrmazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zu\u0026ntilde;iga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17:122.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChang Y, Kim CK, Kim M-K, Seo W-K, Oh K. Insulin resistance is associated with poor functional outcome after acute ischemic stroke in non-diabetic patients. Sci Rep. 2021;11:1229.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQin W, Weng J. Hepatocyte NLRP3 interacts with PKCε to drive hepatic insulin resistance and steatosis. Sci Bull. 2023;68:1413\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhafoury R, Malek M, Ismail-Beigi F, Khamseh ME. Role of Residual Inflammation as a Risk Factor Across Cardiovascular-Kidney-Metabolic (CKM) Syndrome: Unpacking the Burden in People with Type 2 Diabetes. Diabetes Ther Res Treat Educ Diabetes Relat Disord; 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuniyappa R, Lee S, Chen H, Quon MJ. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab. 2008;294:E15\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMatthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28:412\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilliams KV, Erbey JR, Becker D, Arslanian S, Orchard TJ. Can clinical factors estimate insulin resistance in type 1. diabetes? Diabetes. 2000;49:626\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong J, Ma R, Yin L. Associations between estimated glucose disposal rate and arterial stiffness and mortality among US adults with non-alcoholic fatty liver disease. Front Endocrinol. 2024;15:1398265.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Z, Zhao L, Lu Y, Xiao Y, Zhou X. Insulin resistance assessed by estimated glucose disposal rate and risk of incident cardiovascular diseases among individuals without diabetes: findings from a nationwide, population based, prospective cohort study. Cardiovasc Diabetol. 2024;23:194.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo L, Zhang J, An R, Wang W, Fen J, Wu Y, et al. The role of estimated glucose disposal rate in predicting cardiovascular risk among general and diabetes mellitus population: a systematic review and meta-analysis. BMC Med. 2025;23:234.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTian J, Chen H, Luo Y, Zhang Z, Xiong S, Liu H. Association between estimated glucose disposal rate and prediction of cardiovascular disease risk among individuals with cardiovascular-kidney-metabolic syndrome stage 0\u0026ndash;3: a nationwide prospective cohort study. Diabetol Metab Syndr. 2025;17:58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort Profile: The China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43:61\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61:344\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi H, Zheng D, Li Z, Wu Z, Feng W, Cao X, et al. Association of Depressive Symptoms With Incident Cardiovascular Diseases in Middle-Aged and Older Chinese Adults. JAMA Netw Open. 2019;2:e1916591.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie W, Zheng F, Yan L, Zhong B. Cognitive Decline Before and After Incident Coronary Events. J Am Coll Cardiol. 2019;73:3041\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao K, Cao L-F, Ma W-Z, Gao Y-J, Luo M-S, Zhu J, et al. Association between sarcopenia and cardiovascular disease among middle-aged and older adults: Findings from the China health and retirement longitudinal study. EClinicalMedicine. 2022;44:101264.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilliams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur Heart J. 2018;39:3021\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu J, Yi Q, Chen G, Hou L, Liu Q, Xu Y, et al. The visceral adiposity index and risk of type 2 diabetes mellitus in China: A national cohort analysis. Diabetes Metab Res Rev. 2022;38:e3507.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLevey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSetiawan KE, Kurniawan A, Chowanda A, Suhartono D. Clustering models for hospitals in Jakarta using fuzzy c-means and k-means. Procedia Comput Sci. 2023;216:356\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi M, Chi X, Wang Y, Setrerrahmane S, Xie W, Xu H. Trends in insulin resistance: insights into mechanisms and therapeutic strategy. Signal Transduct Target Ther. 2022;7:216.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKosmas CE, Bousvarou MD, Kostara CE, Papakonstantinou EJ, Salamou E, Guzman E. Insulin resistance and cardiovascular disease. J Int Med Res. 2023;51:3000605231164548.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNakashima A, Kato K, Ohkido I, Yokoo T. Role and Treatment of Insulin Resistance in Patients with Chronic Kidney Disease: A Review. Nutrients. 2021;13:4349.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNdumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, et al. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. Circulation. 2023;148:1606\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLarkin H. Here\u0026rsquo;s What to Know About Cardiovascular-Kidney-Metabolic Syndrome, Newly Defined by the AHA. JAMA. 2023;330:2042\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMertens J, Roosens L, Braspenning R, Vandebeeck J, Francque S, De Block C. The 13C Glucose Breath Test Accurately Identifies Insulin Resistance in People With Type 1 Diabetes. J Clin Endocrinol Metab. 2025;110:e432\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHong D, Li X, Sheng G, Yang H, Wang W, Zou Y. Comparing the impact of cumulative insulin resistance surrogates exposure on stroke: optimizing prevention strategies. Lipids Health Dis. 2025;24:158.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong B, Chen Y, Yang X, Chen Z, Zhang H, Gao Y, et al. Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0\u0026ndash;3 and the development of a machine learning prediction model: a nationwide prospective cohort study. Cardiovasc Diabetol. 2025;24:163.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYao J, Zhou F, Ruan L, Liang Y, Zheng Q, Shao J, et al. Association between estimated glucose disposal rate control level and stroke incidence in middle-aged and elderly adults. J Diabetes. 2024;16:e13595.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan L, Zhou Z, Wu X, Qiu Y, Liu Z, Luo L, et al. Association between the changes in the estimated glucose disposal rate and new-onset cardiovascular disease in middle-aged and elderly individuals: A nationwide prospective cohort study in China. Diabetes Obes Metab. 2025;27:1859\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerra CC, Rossi MC, Mirani M, Ceccarelli Ceccarelli D, Romano C, Sassi L, et al. Real world effectiveness of subcutaneous semaglutide in type 2 diabetes: A retrospective, cohort study (Sema-MiDiab01). Front Endocrinol. 2022;13:1099451.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTan J, Li X, Dou N. Insulin Resistance Triggers Atherosclerosis: Caveolin 1 Cooperates with PKCzeta to Block Insulin Signaling in Vascular Endothelial Cells. Cardiovasc Drugs Ther. 2024;38:885\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo C, Han S, Shen X, Wu H, Zhou J, Liu B. Sex-specific associations between estimated glucose disposal rate and cognitive decline in middle-aged and older adults in China: a longitudinal cohort study. Front Aging Neurosci. 2025;17:1544352.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Li H, Chen X, Liang X. Association between various insulin resistance indices and cardiovascular disease in middle-aged and elderly individuals: evidence from two prospectives nationwide cohort surveys. Front Endocrinol. 2024;15:1483468.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMolecular Regulation and Therapeutic Targeting of VLDL Production in Cardiometabolic Disease. - Cellular and Molecular Gastroenterology and Hepatology [Internet]. [cited 2025 June 13]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cmghjournal.org/article/S2352-345X(24)00164-4/fulltext\u003c/span\u003e\u003cspan address=\"https://www.cmghjournal.org/article/S2352-345X(24)00164-4/fulltext\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEngin AB. What Is Lipotoxicity? In: Engin AB, Engin A, editors. Obes Lipotoxicity [Internet]. Cham: Springer International Publishing; 2017 [cited 2025 July 2]. pp. 197\u0026ndash;220. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-48382-5_8\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-48382-5_8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKing GL, Park K, Li Q. Selective Insulin Resistance and the Development of Cardiovascular Diseases in Diabetes: The 2015 Edwin Bierman Award Lecture. Diabetes. 2016;65:1462\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDi Pino A, DeFronzo RA. Insulin Resistance and Atherosclerosis: Implications for Insulin-Sensitizing Agents. Endocr Rev. 2019;40:1447\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHill MA, Yang Y, Zhang L, Sun Z, Jia G, Parrish AR, et al. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metabolism. 2021;119:154766.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarg SS, Kushwaha K, Dubey R, Gupta J. Association between obesity, inflammation and insulin resistance: Insights into signaling pathways and therapeutic interventions. Diabetes Res Clin Pract [Internet]. 2023 [cited 2025 June 13];200. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.diabetesresearchclinicalpractice.com/article/S0168-8227(23)00453-9/abstract\u003c/span\u003e\u003cspan address=\"https://www.diabetesresearchclinicalpractice.com/article/S0168-8227(23)00453-9/abstract\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen L, Chen R, Wang H, Liang F. Mechanisms Linking Inflammation to Insulin Resistance. Int J Endocrinol. 2015;2015:508409.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChaudhuri J, Bains Y, Guha S, Kahn A, Hall D, Bose N, et al. The Role of Advanced Glycation End Products in Aging and Metabolic Diseases: Bridging Association and Causality. Cell Metab. 2018;28:337\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu X, Pan X, Kang J, Huang Y, Ren J, Pan J, et al. Ferulic acid inhibits ox-LDL-induced ferroptosis and apoptosis in RAW 264.7 cells via the HIF-1 signaling pathway. Front Pharmacol. 2025;16:1524736.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYe W, Han K, Xie M, Li S, Chen G, Wang Y, et al. Mitochondrial energy metabolism in diabetic cardiomyopathy: Physiological adaption, pathogenesis, and therapeutic targets. Chin Med J (Engl). 2024;137:936\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCadenas S. ROS and redox signaling in myocardial ischemia-reperfusion injury and cardioprotection. Free Radic Biol Med. 2018;117:76\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\n \u003ctable 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 according to eGDR by K-means clustering analysis in CKM syndrome stages 0\u0026ndash;3\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;2862)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass1 (N\u0026thinsp;=\u0026thinsp;1388)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass2 (N\u0026thinsp;=\u0026thinsp;569)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass3 (N\u0026thinsp;=\u0026thinsp;905)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\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\"\u003e\n \u003cp\u003eAge,years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.00 [51.00, 63.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.00 [49.00, 61.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.00 [52.00, 65.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.00 [53.00, 65.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1299 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e619 (44.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e285 (50.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e395 (43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1563 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e769 (55.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e284 (49.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e510 (56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2509 (87.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1229 (88.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e491 (86.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e789 (87.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e353 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e159 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78 (13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73 ( 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32 ( 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 ( 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 ( 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElementary school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1922 (67.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e882 (63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e399 (70.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e641 (70.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e867 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e474 (34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e154 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e239 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHukou status,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2462 (86.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1215 (87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e484 (85.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e763 (84.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e400 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e173 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e142 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e872 (30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e422 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e203 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e247 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204 ( 7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81 ( 5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44 ( 7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79 ( 8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever Smoked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1786 (62.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e885 (63.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e322 (56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e579 (64.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e895 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e418 (30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e279 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e190 ( 6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70 ( 5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35 ( 6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85 ( 9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1777 (62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e900 (64.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e336 (59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e541 (59.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCKM syndrome stages, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114 ( 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108 ( 7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 ( 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 ( 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e356 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e302 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54 ( 9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 ( 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e833 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e345 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e175 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e313 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1559 (54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e633 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e334 (58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e592 (65.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI,kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.28 [21.09, 25.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.16 [20.39, 24.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.61 [20.93, 26.41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.23 [23.00, 27.52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP,mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125.33 [114.00, 140.33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116.33 [108.33, 125.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126.67 [118.33, 135.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145.00 [133.33, 156.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBP,mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.33 [67.33, 82.33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.67 [63.67, 75.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.33 [68.00, 80.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.33 [75.33, 90.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2702 (94.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1361 (98.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e534 (93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e807 (89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e160 ( 5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27 ( 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35 ( 6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1810 (63.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1303 (93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e480 (84.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1052 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85 ( 6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89 ( 15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e878 (97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2627 (91.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1329 (95.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e534 (93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e764 (84.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e235 ( 8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59 ( 4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35 ( 6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKidney disease,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2770 (96.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1332 (96.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e554 (97.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e884 (97.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92 ( 3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56 ( 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15 ( 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21 ( 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiver disease,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2787 (97.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1354 (97.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e549 (96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e884 (97.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75 ( 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34 ( 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20 ( 3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21 ( 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipid-lowering treatment,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2747 (96.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1362 (98.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e553 (97.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e832 (91.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115 ( 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 ( 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 ( 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73 ( 8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntihypertensive treatment,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2431 (84.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1385 (99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e540 (94.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e506 (55.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e431 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 ( 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29 ( 5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e399 (44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypoglycemic treatment,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2760 (96.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1371 (98.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e546 (96.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e843 (93.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102 ( 3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17 ( 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23 ( 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62 ( 6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.10 [4.90, 5.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.10 [4.80, 5.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.20 [4.90, 5.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.20 [5.00, 5.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFBG, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102.60 [95.22, 112.32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.44 [93.42, 108.54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103.86 [95.58, 115.56]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104.94 [98.28, 118.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105.32 [74.34, 155.76]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.58 [69.03, 134.52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101.78 [71.68, 154.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126.56 [87.61, 184.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e191.37 [168.17, 216.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e185.57 [164.30, 209.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e191.37 [169.33, 218.43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198.33 [175.13, 224.61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-c, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.10 [40.59, 59.92]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.42 [42.53, 61.86]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.48 [39.43, 61.47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.39 [38.27, 55.28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-c, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115.21 [94.33, 139.18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111.92 [92.78, 134.15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115.98 [92.40, 140.34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120.62 [97.81, 144.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBUN,mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.96 [12.50, 17.93]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.97 [12.58, 17.79]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.90 [12.38, 18.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.01 [12.52, 18.15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatinine,mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73 [0.64, 0.86]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75 [0.63, 0.85]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73 [0.64, 0.86]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73 [0.66, 0.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUric acid, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.17 [3.46, 5.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.02 [3.35, 4.78]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.24 [3.48, 5.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.38 [3.64, 5.31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGFR, mL/min/1.73m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.51 [86.87, 103.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.17 [87.49, 104.77]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.38 [87.60, 103.87]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.37 [84.93, 101.35]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCumulative eGDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.22 [21.64, 32.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.32 [30.62, 33.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.18 [24.52, 27.78]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.52 [17.49, 21.43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2458 (85.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1264 (91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e476 (83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e718 (79.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e404 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124 ( 8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e187 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeart disease,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2608 (91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1308 (94.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e504 (88.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e796 (88.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e254 ( 8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80 ( 5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStroke,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2685 (93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1340 (96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e532 (93.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e813 (89.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e177 ( 6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48 ( 3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37 ( 6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eBMI, body Mass Index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin A1c;FBG, fasting blood glucose; TG, triglycerides; TC, total cholesterol;HDL-c,high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; BUN,blood urea nitrogen; eGFR, estimated glomerular filtration rate; eGDR, estimated glucose disposal rate; CVD, cardiovascular diseases.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLogistic analysis for the association between different eGDR change and CVD.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrude model\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eChange in the eGDR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99 (1.49\u0026ndash;2.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90 (1.42\u0026ndash;2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82 (1.36\u0026ndash;2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65 (2.08\u0026ndash;3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.43 (1.90\u0026ndash;3.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90 (1.41\u0026ndash;2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eCumulative eGDR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60 (0.46\u0026ndash;0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62 (0.47\u0026ndash;0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75 (0.56\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40 (0.30\u0026ndash;0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44 (0.33\u0026ndash;0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56 (0.40\u0026ndash;0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33 (0.24\u0026ndash;0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36 (0.26\u0026ndash;0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47 (0.32\u0026ndash;0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65 (0.58\u0026ndash;0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67 (0.60\u0026ndash;0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74 (0.65\u0026ndash;0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eHeart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eChange in the eGDR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.11 (1.49\u0026ndash;2.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06 (1.46\u0026ndash;2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01 (1.42\u0026ndash;2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.24 (1.66\u0026ndash;3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07 (1.52\u0026ndash;2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78 (1.23\u0026ndash;2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eCumulative eGDR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74 (0.53\u0026ndash;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78 (0.56\u0026ndash;1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89 (0.62\u0026ndash;1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47 (0.32\u0026ndash;0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51 (0.35\u0026ndash;0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60 (0.39\u0026ndash;0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43 (0.29\u0026ndash;0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47 (0.32\u0026ndash;0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56 (0.36\u0026ndash;0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71 (0.63\u0026ndash;0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74 (0.65\u0026ndash;0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 (0.67\u0026ndash;0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eChange in the eGDR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94 (1.24\u0026ndash;3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81 (1.16\u0026ndash;2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67 (1.06\u0026ndash;2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.16 (2.22\u0026ndash;4.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.89 (2.02\u0026ndash;4.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95 (1.26\u0026ndash;3.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eCumulative eGDR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57 (0.39\u0026ndash;0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57 (0.39\u0026ndash;0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75 (0.49\u0026ndash;1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35 (0.23\u0026ndash;0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38 (0.24\u0026ndash;0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55 (0.33\u0026ndash;0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24 (0.15\u0026ndash;0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26 (0.16\u0026ndash;0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40 (0.22\u0026ndash;0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59 (0.51\u0026ndash;0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61 (0.52\u0026ndash;0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73 (0.60\u0026ndash;0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eCrude model, unadjusted for covariates; Model 1, adjusted for age and gender; Model 2, adjusted for age, gender, hukou status, education status, marital status,smoking status, drinking status, TC, LDL-c, eGFR,diabetes, lipid-lowering therapy, antihypertensive therapy, and diabetes treatment.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eeGDR, estimated glucose disposal rate; CVD, cardiovascular diseases; Q1, Quartile 1; Q2, Quartile 2; Q3, Quartile 3; Q4, Quartile 4; OR, odds ratio; CI, confidence interval; SD, standard deviation.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular disease, Cardiovascular–kidney–metabolic syndrome, Insulin resistance, Estimated glucose disposal rate, CHARLS, Prospective cohort study","lastPublishedDoi":"10.21203/rs.3.rs-7331938/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7331938/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and aims\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCardiovascular–kidney–metabolic syndrome (CKM) significantly increases the burden of cardiovascular disease (CVD), particularly in China,which has a rapidly aging population.The estimated glucose disposal rate (eGDR) is a reliable indicator for assessing insulin resistance (IR),but its dynamic changes and association with the risk of new-onset CVD in patients with CKM syndrome have not been fully elucidated.The aim of this study was to investigate the associations between dynamic changes in and cumulative of eGDR (cumeGDR) and the risk of new-onset CVD in Chinese adults with CKM syndrome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e：\u003c/p\u003e\n\u003cp\u003eA total of 2862 patients with CKM syndrome (stages 0–3) without CVD at baseline from the China Health and Retirement Longitudinal Study (CHARLS) were enrolled. K-means clustering was used to measure the change in eGDR from 2012 to 2015, and the cumulative eGDR level was calculated. Logistic regression, restricted cubic splines (RCS), and subgroup analysis were used to explore the potential associations between changes in the eGDR and the risk of new-onset CVD (including heart disease and stroke) in patients with CKM syndrome stages 0–3.\u003cbr\u003e\n\u003cstrong\u003eResults\u003c/strong\u003e：\u003c/p\u003e\n\u003cp\u003eDuring the 3-year follow-up period, 404 (14.1%) CVD events occurred, including 254 heart disease cases and 177 stroke cases. After adjusting for confounding factors, compared with the group with persistently high eGDR level (Class 1), the groups with significantly decreased eGDR level (Class 2) and persistently low eGDR level (Class 3) had a significantly increased CVD risk (Class 2: OR = 1.82 [1.36–2.45],P\u0026lt;0.001;Class 3: OR = 1.90 [1.41–2.56],P\u0026lt;0.001). Further RCS regression analysis revealed a negative linear association between the cumulative eGDR and CVD risk(P for overall \u0026lt;0.001, nonlinear P=0.922).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e：\u003c/p\u003e\n\u003cp\u003ePersistently low eGDR level are associated with an increased risk of new-onset CVD in those with CKM syndrome stages 0–3. Continuous dynamic monitoring of the eGDR may help identify high-risk individuals with CKM syndrome stages 0–3 and provide critical evidence for early intervention.\u003c/p\u003e","manuscriptTitle":"Changes in the Estimated Glucose Disposal Rate and Incident Cardiovascular Disease in Patients with Cardiovascular–Kidney–Metabolic Syndrome Stages 0–3: A Prospective Cohort Study in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 10:57:39","doi":"10.21203/rs.3.rs-7331938/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-23T06:18:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-22T13:49:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-15T09:36:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"83534278285212910481382208031829072456","date":"2025-09-09T08:45:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227152711504972020567838795151155378529","date":"2025-09-04T03:36:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-04T02:31:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-21T04:53:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-20T11:56:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-20T11:55:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-08-09T07:05:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3a6d7fc-edc2-47ba-940c-f2ea44dc4020","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T15:59:29+00:00","versionOfRecord":{"articleIdentity":"rs-7331938","link":"https://doi.org/10.1186/s12872-025-05300-8","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2025-11-26 15:57:01","publishedOnDateReadable":"November 26th, 2025"},"versionCreatedAt":"2025-09-11 10:57:39","video":"","vorDoi":"10.1186/s12872-025-05300-8","vorDoiUrl":"https://doi.org/10.1186/s12872-025-05300-8","workflowStages":[]},"version":"v1","identity":"rs-7331938","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7331938","identity":"rs-7331938","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}