Assessment of seven insulin resistance surrogate indexes for predicting cardiometabolic multimorbidity among Chinese middle-aged and older adults: a national prospective cohort study.

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Learn more: PMC Disclaimer | PMC Copyright Notice Cardiovasc Diabetol . 2026 Apr 5;25:121. doi: 10.1186/s12933-026-03168-2 Search in PMC Search in PubMed View in NLM Catalog Add to search Assessment of seven insulin resistance surrogate indexes for predicting cardiometabolic multimorbidity among Chinese middle-aged and older adults: a national prospective cohort study Jun Lai Jun Lai 1 Department of Pharmacology, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, 76 Yanta West Road, Xi’an, 710061 Shaanxi China 2 Department of Pharmacy, the Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, 341000 Jiangxi People’s Republic of China Find articles by Jun Lai 1, 2, ✉ , Zongyan Liu Zongyan Liu 2 Department of Pharmacy, the Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, 341000 Jiangxi People’s Republic of China Find articles by Zongyan Liu 2 , Huajie Wang Huajie Wang 2 Department of Pharmacy, the Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, 341000 Jiangxi People’s Republic of China Find articles by Huajie Wang 2 , Xiaoqing Kong Xiaoqing Kong 3 National Pharmaceutical Engineering Center for Solid Preparation of Chinese Herbal Medicine, State Key Laboratory for the Modernization of Classical and Famous Prescriptions of Chinese Medicine, Key Laboratory of Modern Preparation of TCM, Ministry of Education, Jiangxi University of Chinese Medicine, Nanchang, 330006 Jiangxi People’s Republic of China Find articles by Xiaoqing Kong 3 , Yufeng Wei Yufeng Wei 2 Department of Pharmacy, the Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, 341000 Jiangxi People’s Republic of China Find articles by Yufeng Wei 2 , Yongxiao Cao Yongxiao Cao 1 Department of Pharmacology, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, 76 Yanta West Road, Xi’an, 710061 Shaanxi China Find articles by Yongxiao Cao 1, ✉ Author information Article notes Copyright and License information 1 Department of Pharmacology, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, 76 Yanta West Road, Xi’an, 710061 Shaanxi China 2 Department of Pharmacy, the Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, 341000 Jiangxi People’s Republic of China 3 National Pharmaceutical Engineering Center for Solid Preparation of Chinese Herbal Medicine, State Key Laboratory for the Modernization of Classical and Famous Prescriptions of Chinese Medicine, Key Laboratory of Modern Preparation of TCM, Ministry of Education, Jiangxi University of Chinese Medicine, Nanchang, 330006 Jiangxi People’s Republic of China ✉ Corresponding author. Received 2025 Nov 2; Accepted 2026 Mar 23; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13067570  PMID: 41937148 Abstract Background Cardiometabolic multimorbidity (CMM) poses a mounting health challenge worldwide. Seven surrogate indexes of insulin resistance (IR)—the triglyceride-glucose index (TyG), TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), Chinese visceral adiposity Index (CVAI), Metabolic score for IR (METS-IR), Atherogenic index of plasma (AIP), and estimated glucose disposal rate (eGDR)—are well-established. However, comparative studies evaluating their predictive capacity for CMM incidence in Chinese middle-aged and older adults remain scarce. This study aimed to assess the associations between these seven IR indexes and CMM risk within this population and determine their relative predictive abilities. Methods Utilizing the China Health and Retirement Longitudinal Study (CHARLS) 2011–2020 datasets, this prospective cohort investigation assessed Chinese participants aged ≥ 45 years. We applied Kaplan–Meier curve plotting, multivariable Cox proportional hazards models, and restricted cubic splines (RCS) to quantify associations of insulin resistance surrogate indexes with CMM risk. Predictive accuracy was appraised via time-dependent receiver operating characteristic (ROC) curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Subgroup evaluations further verified findings robustness. Results During a median follow-up of 9 years, 1043 (14.49%) of the 7197 participants developed CMM. After adjusting for potential confounders, we observed that each standard deviation (SD) increase in eGDR was associated with a reduced risk of CMM, with an adjusted hazard ratio (HR) of 0.834 [95% confidence interval (CI): 0.781–0.891]. In contrast, each SD increase in TyG, TyG-BMI, TyG-WC, METS-IR, AIP and CVAI were associated with an increased risk of CMM. Restricted cubic spline analyses showed that TyG-BMI, TyG-WC, METS-IR, and eGDR were nonlinear associated with incident new-onset CMM ( P -nonlinearity < 0.05). eGDR showed a L-shaped association ( P -nonlinearity < 0.05), with CMM risk decreasing until the inflection point at 11.82 (HR = 0.758; 95% CI: 0.702–0.818). Conversely, TyG-WC displayed a U-shaped relationship (inflection point: 572.14). Moreover, the time-dependent AUC analysis revealed that eGDR exhibited superior predictive discrimination (AUC 0.68–0.76) during early follow-up and across all intervals. The optimal cut-off value for eGDR was determined to be 7.64. Conclusion All seven insulin resistance surrogate indexes independently demonstrated elevated CMM risk. In the context of Chinese middle-aged and elderly cohorts, eGDR demonstrated a notable predictive capacity for CMM. ROC-derived cut-offs (eGDR < 7.64) are utilised for identifying high-risk individuals requiring intervention. Spline-derived thresholds (eGDR = 11.82) serve as therapeutic targets for risk reduction. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1186/s12933-026-03168-2. Keywords: Insulin resistance surrogate index, Cardiometabolic multimorbidity , China Health and Retirement Longitudinal Study, Middle-aged and older adult Research insights What is currently known about this topic? Cardiometabolic multimorbidity (CMM) poses a mounting health challenge worldwide. Seven surrogate indexes of insulin resistance (IR)—TyG, TyG-BMI, TyG-WC, CVAI, METS-IR, AIP, and eGDR—are well-established. However, comparative studies evaluating their predictive capacity for CMM incidence in Chinese middle-aged and older adults remain scarce. What is the key research question? Which of the seven established insulin resistance surrogate indexes (TyG, TyG-BMI, TyG-WC, eGDR, CVAI, METS-IR, and AIP) best predicts incident cardiometabolic multimorbidity (CMM) in middle-aged and older Chinese adults? What is new? This study shows that TyG-BMI, TyG-WC, METS-IR, and eGDR were nonlinear associated with incident new-onset CMM, whereas TyG, AIP, and CVAI were linearly positive associated with CMM risk. Moreover, eGDR showed superior predictive accuracy among IR surrogate indexes, with TyG-WC, CVAI, METS-IR, TyG-BMI, TyG, and AIP following in descending order of performance. The METS-IR exhibited the highest NRI, followed by CVAI, TyG-WC, TyG-BMI and eGDR. ROC-derived cut-offs (eGDR < 7.64) are utilised for identifying high-risk individuals requiring intervention. Spline-derived thresholds (eGDR = 11.82) serve as therapeutic targets for risk reduction. How might this study influence clinical practice? Among middle-aged and older Chinese populations, eGDR exhibited superior predictive performance for CMM. This finding enables the development and application of early preventive and interventional measures. Introduction The escalating global trend of population ageing has catalysed the rapid emergence of multimorbidity as a substantial healthcare challenge presenting substantial implications for global health systems [ 1 ]. Cardiometabolic multimorbidity (CMM), characterised by the coexistence of two or more cardiometabolic diseases (CMDs) such as diabetes, heart disease, and stroke, represents one of the most clinically significant multimorbidity patterns worldwide [ 2 , 3 ]. Accumulating evidence indicates CMM correlates with adverse trajectories of chronic conditions including physical disability, dementia, cognitive impairment, and depression [ 4 – 6 ]. Moreover, compared with isolated CMDs, CMM demonstrates a multiplicative increase in mortality risk and significant reduction in life expectancy [ 2 ]. Nationally representative surveys report rising CMM prevalence among Chinese elderly populations—increasg from 11.6 to 16.9%—with continuing upward trajectories [ 7 ]. Consequently, timely identification and management of CMM holds paramount importance for promoting healthy ageing and alleviating societal healthcare burdens. Insulin resistance (IR) is a pivotal pathophysiological mechanism underpinning the development and progression of individual CMDs and is increasingly recognized as a central driver of their co-occurrence in CMM [ 8 – 10 ]. Consequently, reliable and accessible IR biomarkers are crucial for risk stratification. The hyperinsulinemic-euglycemic clamp (HEC) remains the gold standard for quantifying insulin resistance (IR). However, its invasive nature, technical complexity demanding specialized expertise, and substantial costs severely limit clinical utility, rendering it inapplicable for population-level studies or routine clinical screening [ 11 ]. This has spurred the development and validation of numerous surrogate IR indexes derived from routinely measured clinical and biochemical parameters. Promising indexes include the Triglyceride-Glucose Index (TyG) [ 12 ], TyG combined with body composition measures (TyG-BMI, TyG-WC) [ 13 , 14 ], the estimated Glucose Disposal Rate (eGDR) [ 15 ], the Chinese Visceral Adiposity Index (CVAI) [ 16 ], the Metabolic Score for IR (METS-IR) [ 17 , 18 ], and the Atherogenic Index of Plasma (AIP) [ 19 ]. These surrogates demonstrate strong correlations with clamp-measured IR. In recent years, these surrogates have been widely used as predictive tools owing to their robust performance and practicality [ 8 , 20 – 26 ]. Specifically, Jiang et al. [ 20 ] demonstrated significant associations between six insulin resistance (IR) surrogate indices and elevated stroke risk among dysglycaemic populations. The eGDR was identified as a particularly promising predictor for stroke risk in middle-aged and elderly Chinese cohorts. Restricted cubic spline (RCS) analysis demonstrated a linear inverse correlation between eGDR and stroke risk. By contrast, recent evidence indicates that reduced baseline eGDR levels are associated with increased stroke incidence, with RCS modelling revealing an L-shaped relationship characterized by a critical threshold at 10.53. Maintaining eGDR above this value may attenuate stroke occurrence [ 22 ]. Although prior studies established associations between these IR indexes and single cardiometabolic disease (CMD) endpoints, a key limitation persists: the absence of time-to-event analysis. Divergent RCS curve-fitting patterns between these studies underscore the need for further investigation into their independent and comparative relationships with incident CMM, particularly through large-scale prospective cohorts. In a recent NHANES-based study, Zhang et al. demonstrated that among 1,093 U.S. patients with CMM, only the TyG index showed significant associations with both cardiovascular and all-cause mortality. Furthermore, the TyG index exhibited significantly higher predictive accuracy for all-cause mortality (assessed by AUC) than both the METS-IR and HOMA-IR [ 27 ]. Nevertheless, as NHANES constitutes a cross-sectional survey, causal inferences between these indices and mortality outcomes remain limited. The generalizability of these findings to diverse ethnic populations also requires validation. Notably, the authors did not establish optimal cutoff values via ROC analysis using Youden's index, warranting further investigation. In contrast, a prospective UK Biobank study identified TyG-WHtR and TyG-WC as significant predictors of CMM incidence and progression, outperforming other IR indices. These associations were quantitatively validated through NRI and IDI metrics. However, the clinical applicability of these indices may be constrained by the absence of ROC-derived optimal discriminative cutoffs for CMM risk stratification [ 21 ]. Xiao et al. subsequently confirmed associations between IR surrogates and incident CMM in middle-aged and older Chinese adults, with the CVAI demonstrating superior predictive capability. Methodological limitations, however—including a restricted follow-up period (2011–2018), lack of time-to-event analysis, and unmeasured confounders such as physical activity—temper the interpretability of these findings [ 23 ]. Complementing these reports, a recent cohort study revealed that elevated TyG-related indices combined with reduced eGDR independently and jointly predicted increased CMM risk. The synergistic use of these markers may therefore enhance early CMM identification and prevention strategies. A notable limitation merits consideration: the current absence of clinically established thresholds for TyG-related indices and eGDR necessitates reliance on median-based stratification. While methodologically sound, this approach could fail to identify biologically optimal discriminative cutoffs for risk assessment [ 8 ]. To address these evidence gaps, we conducted a prospective cohort study utilizing the CHARLS database. Our primary objectives were: (1) To compare the prognostic performance of seven IR indices—eGDR, CVAI, TyG, TyG-BMI, TyG-WC, AIP, and METS-IR—for CMM risk prediction. The comparative utility of these indices, particularly emerging measures such as eGDR, CVAI, and METS-IR, remains underexplored for CMM risk stratification specifically in Chinese middle-aged and older adults. Population-specific validation is imperative given the distinct pathophysiology of cardiometabolic disease in Asian populations, characterized by prominent visceral adiposity and beta-cell dysfunction at lower body mass index thresholds [ 28 ]; (2) To implement time-to-event analysis over an extended follow-up period (2011–2020) for comprehensive assessment of IR-CMM relationships; (3) To define optimal discriminative thresholds using ROC analysis and evaluate the incremental value of combined IR indices; (4) To characterize IR-CMM associations through RCS modeling. Identifying novel predictors of CMM is essential for implementing tertiary preventive strategies in China. Consequently, this study aimed to identify an optimal biomarker associated with CMM incidence in Chinese middle-aged and older adults. Such comparative analyses provide critical evidence to guide the development of optimized risk assessment strategies for this high-disease-burden demographic. Methods Study design and population The data were sourced from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort survey of middle-aged and older Chinese adults (aged ≥ 45 years). Design specifics and inclusion criteria are documented elsewhere [ 29 ]. The dataset under consideration comprises baseline and longitudinal information gathered through structured interviews combined with clinical assessments. The study encompasses a wide range of socio-demographic characteristics, health indicators, and lifestyle variables. The survey protocol was conducted in accordance with the Declaration of Helsinki and received approval from Peking University's Biomedical Ethics Review Board (IRB 00001052–11015). Prior to inclusion in the study, all participants provided written informed consent. Further details pertaining to CHARLS can be accessed via the official website ( http://charls.pku.edu.cn/en ). CHARLS was conducted between June 2011 and March 2012. Biennial follow-up evaluations were conducted through in-person interviews. Standardised data acquisition was ensured by trained interviewers utilising computer-assisted methods [ 30 ]. The present investigation incorporated subjects assessed during the 2011–2012 period into the baseline group. Subsequent data collection took place in 2013, 2015, 2018, and 2020. The initial cohort comprised 17,708 individuals from the 2011 baseline. In order to refine the analysis, specific exclusion criteria were applied sequentially: (1) existing CMM at baseline; (2) baseline age < 45 years; (3) incomplete data for any of the seven insulin resistance surrogate indexes at baseline; (4) absent socio-demographic, health-related, anthropometric, or other biomarker information at baseline; (5) unavailable CMM status during follow-up. Consequently, the final analytical sample comprised 7197 subjects. As illustrated in Fig. 1 , the exclusion process is a crucial aspect of the overall procedure. Fig. 1. Open in a new tab Flow chart of inclusion and exclusion criteria of participants.Abbreviations: CMM, cardiometabolic multimorbidity Data collection and measurement During the baseline assessment, demographic information (age, gender, educational level, marital status, residence type), health behaviors (smoking and alcohol consumption), and medical history (diabetes, hypertension) were gathered by interviewers via questionnaires. Educational level was divided into four categories: no formal schooling, primary school, middle school, high school or above. Marital status was grouped as married versus other (separated, divorced, widowed, never married). Smoking was defined as having smoked ≥ 100 cigarettes over a lifetime. Participants were then categorized into non-smokers, former smokers, or current smokers. Drinking status was categorized into: never drinkers (rare or no alcohol intake, with monthly consumption under once), former drinkers (previous monthly drinkers who quit within the past year), and current drinkers (monthly intake ≥ once). Trained staff took anthropometric measurements, including height, weight, waist circumference (WC), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Participant blood pressure (BP) was averaged from three readings using an Omron HEM-7200 sphygmoanometer. Venous blood samples were collected from all participants following ≥ 8 h of fasting. Samples were then stored immediately at a temperature of − 20 °C, followed by cryopreserved transport to the Beijing laboratory for standardized biomarker analysis. Laboratory analysis of these samples included fasting plasma glucose (FPG), glycosylated hemoglobin A1c (HbA1c), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), serum creatinine (Scr), serum uric acid (SUA), blood urea nitrogen (BUN), and high-sensitivity C-reactive protein (hs-CRP). The diagnostic criteria for covariates employed in this study align with definitions established in prior CHARLS-based literature [ 31 , 32 ]. Definition Hypertension was determined based on any of the following criteria: a self-reported diagnosis provided by a physician, current use of antihypertensive medications, or measured blood pressure values with systolic blood pressure (SBP) ≥ 140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg [ 33 ]. Similarly, diabetes was diagnosed if participants reported a physician-confirmed diagnosis, were using glucose-lowering medications, or presented with fasting blood glucose (FBG) levels ≥ 126 mg/dL, 2-h plasma glucose concentrations ≥ 200 mg/dL, and/or glycated hemoglobin (HbA1c) levels of at least 6.5% at baseline [ 34 ]. The primary outcome of this study was the incidence of CMM events, characterized by the coexistence of two or more CMDs, including diabetes, heart disease, and stroke [ 31 , 32 , 35 ]. The diagnosis of heart disease and stroke is based on self-reported data or related medication use collected at baseline and follow-up surveys. Data on these conditions were gathered using standardized questions, such as: “Have you ever been diagnosed by a physician with coronary heart disease, angina, heart failure, or any other cardiac condition?” and “Have you ever been diagnosed by a physician with a stroke?” [ 36 , 37 ]. The CMM diagnostic criteria employed in this study are consistent with those utilised in previous literature based on CHARLS data [ 23 , 31 , 32 ]. The timing of events was calculated as the period between the preceding interview and the one during which a new diagnosis of CMM was recorded. For individuals who did not report CMM throughout the follow-up period, the duration of monitoring was defined as the span from the initial assessment to the date of their last survey participation [ 30 , 31 ]. Insulin resistance (IR) was evaluated through multiple established surrogate markers derived from easily accessible clinical measurements. The principal metric employed was the eGDR index, which was determined using WC, hypertension status, and HbA1c levels. Moreover, six additional widely utilised IR metrics were analysed for comparative purposes. The eGDR index and the other IR markers were computed in accordance with the methodologies outlined in prior research [ 20 , 23 ]. TyG, TyG-BMI, TyG‐WC, eGDR, CVAI, METS-IR, and AIP were calculated using the following equations: Handling of missing data and data preprocessing The degree of missing data in this study is detailed in Table S1 . Additionally, to reduce bias resulting from missing variables, we used multiple imputation by chained equations (MICE) to address the missing data. During data preprocessing, extreme values were detected in BMI and WC indices. A percentile-based Winsorization method was implemented to mitigate outlier effects and improve analytical robustness. Values beyond the 1st and 99th percentiles were replaced with corresponding thresholds, retaining the central 98% distribution. Statistical analysis Continuous variables with a normal distribution were reported as mean ± standard deviation (SD), while those with a skewed distribution were expressed as median with interquartile range (25–75th percentile). Differences between groups were evaluated using either t-tests for normally distributed data or rank sum tests for skewed data. Categorical variables were summarized as frequencies and percentages, with differences examined using chi-squared tests or Fisher’s exact test, as appropriate. The relationships between surrogate indexes of IR and the occurrence of CMM were evaluated using Cox proportional hazards regression analyses. Schoenfeld residual analysis was employed to evaluate the proportional hazards assumption underlying Cox regression model. The findings were expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). For each standard deviation (SD) increase in continuous IR-related indexes, hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. Furthermore, these metrics were determined for the second to fourth quartiles of exposures (Q2–Q4), with the first quartile (Q1) serving as the reference group. A directed acyclic graph (DAG; www.dagitty.net ) was constructed to identify potential confounders and to guide covariate selection [ 38 ]. DAG–based variable selection was used to identify a minimally sufficient adjustment set, prioritizing direct causal pathways (Fig. S1 ). Furthermore, covariates were retained if they satisfied at least one of the following criteria for any outcome: (1) Important demographic characteristics. (2) Factors were chosen when adding it to this model and changed the matched odds ratio by at least 10 percent. (3) Factors were chosen when in univariate analysis, their P values were less than 0.1. (4) Factors were chosen based on previous findings and clinical constraints [ 20 , 22 , 23 , 31 , 39 ]. To ensure consistency and reduce residual confounding, all covariates meeting these criteria for any outcome were included in the final models. Three logistic models were used in this study. Model 1 accounted for demographic and lifestyle factors (age, gender, education, marital status, residence, drinking status and smoking status). Model 2 added clinical parameters SBP, DBP, TC, Scr, SUA, BUN, hs-CRP, HbA1c, duration of nighttime sleep, and the presence of pre-existing conditions such as heart disease, hypertension, stroke and diabetes. Model 3 further included the use of antidiabetic and antihypertensive medications. Prior to formal analyses, multicollinearity among all study variables was assessed. The potential for multicollinearity among the variables in each model was assessed using the variance inflation factor (VIF). The VIF values for all variables in each model were below 10, and no significant multicollinearity problems were detected [ 20 ]. The results are shown in Tables S3.1-S3.9. The cumulative risk of developing incident CMM was evaluated across quartiles of surrogate indexes of IR using the Kaplan–Meier survival analysis. The log-rank test was employed to determine statistical significance. Additionally, to assess potential nonlinear relationships between IR surrogates and CMM incidence, we employed Cox proportional hazards regression models with restricted cubic splines (RCS). Knots were positioned at the 5th, 35th, 65th, and 95th percentiles. Analyses adjusted for Model 3 covariates. If a nonlinear relationship was identified, the threshold value was determined by evaluating all possible values and selecting the one that yielded the highest likelihood. Furthermore, subgroup and interaction analyses were performed to further validate the robustness of the interaction effect between all surrogate indexes of IR and the development of CMM. These analyses included subgroups categorized by age (45–59 years, and ≥ 60 years), gender (male, female), marital status, drinking status and smoking status, BMI (< 24 kg/m 2 , ≥ 24 kg/m 2 ) and hypertension. To quantify the discriminative accuracy of the seven insulin resistance indices (TyG, TyG-BMI, TyG-WC, AIP, CVAI, METS-IR, eGDR) for incident CMM during follow-up, we utilized time-dependent receiver operating characteristic (ROC) curves. The area under the curve (AUC), optimal cutoff value, sensitivity, specificity, and Youden's index (sensitivity + specificity − 1) were calculated for each index with a view to predicting CMM risk. Internal validation was subsequently performed via bootstrapping (1000 replicates) to obtain optimism-corrected time-dependent AUC estimates. Furthermore, DeLong's test was employed to detect differences in the AUCs of different IR surrogate indexes. Calibration validation: Model calibration performance was evaluated via calibration curves and complementary metrics including calibration slopes and Brier scores. This integrated bootstrap approach (1000 resamples) provided robust internal validation of model characteristics. Additionally, the net reclassification index (NRI) and integrated discrimination improvement (IDI) were computed to quantify the incremental predictive value of each insulin resistance (IR) surrogate index relative to a baseline model. The baseline model incorporated identical covariates to Model 3. Each IR index (TyG, TyG-BMI, TyG-WC, AIP, METS-IR, CVAI, and eGDR) was sequentially added to this base model. Reported NRI and IDI values reflect the improvement in discriminative performance when each index was included versus the reference model alone. Interpretation followed standard criteria: NRI or IDI > 0 denotes improved discrimination, NRI or IDI < 0 indicates reduced performance, and NRI or IDI = 0 suggests no meaningful improvement. To verify the robustness of the study findings, the following sensitivity analyses were performed: (1) To minimize potential bias attributable to missing information, multiple imputation by chained equations (MICE) was applied. (2) Participants with diabetes, heart disease, or stroke at baseline were excluded. (3) To address potential bias from competing mortality risks, associations were reassessed using Fine-Gray subdistribution hazards regression, treating non-CMM-related deaths as competing events. (4) Incorporation of physical activity, assessed via the International Physical Activity Questionnaire (IPAQ), as an additional covariate. Total weekly activity levels, calculated in MET-hours/week (1 MET-hour = 1 kcal/kg/h) and categorised (low, moderate, high), were included. Given significant missing IPAQ data (57.5%, n = 4140; see Table S15), values were imputed using MICE via the R mice package (five datasets) with auxiliary variables predictive of physical activity and its missingness to minimise bias. All statistical analyses were conducted using R (Version 4.2.2, http://www.R-project.org , The R Foundation) and Free Statistics Software version 2.2 (Beijing Free Clinical Medical Technology Co., Ltd., https://www.clinicalscientists.cn/freestatistics/ ). Statistical significance was defined as a two-sided P -value < 0.05. To account for multiple testing arising from analyses across seven indices, quartile categories, and subgroups, we applied the Bonferroni correction. Results Baseline characteristics of study participants A total of 7197 participants with no CMM were enrolled in the study. Table 1 presents the baseline characteristics of the study population, stratified by CMM diagnosis. The mean age (SD) of the participants was 58.2 ± 8.5 years, with 44.80% of them being male. During the follow-up period, which lasted up to 9.0 years, 1043 (14.49%) of the participants developed CMM. Compared to the non-CMM, participants with CMM were more likely to be older, female and urban residents. Furthermore, at baseline, the CMM group exhibited a higher prevalence of chronic diseases, including hypertension, diabetes, heart disease, and stroke, as well as greater utilisation of antihypertensive and antidiabetic medications. In addition, comparative analysis revealed that participants with CMM exhibited significantly elevated levels of SBP, DBP, BMI, FPG, HbA1C, TC, TG, LDL-C, hs-CRP, SUA, TyG, TyG-BMI, TyG-waist, AIP, METS-IR, and CVAI when compared with those lacking CMM. Additionally, they had significantly lower levels of night sleep duration, HDL-C, and eGDR ( P < 0.05 for all). Table 1. Baseline characteristics of the study participants according to CMM Variables Total (n = 7197) Non-CMM (n = 6154) CMM (n = 1043) P value Age, mean ± SD 58.2 ± 8.5 57.8 ± 8.5 60.7 ± 8.3 < 0.001 Gender, n (%) 0.02 Female 3970 (55.2) 3360 (54.6) 610 (58.5) Male 3227 (44.8) 2794 (45.4) 433 (41.5) Marriage, n (%) < 0.001 Yes 6468 (89.9) 5564 (90.4) 904 (86.7) Others 729 (10.1) 590 (9.6) 139 (13.3) Residence, n (%) 0.036 Urban 2378 (33.0) 2004 (32.6) 374 (35.9) Rural 4819 (67.0) 4150 (67.4) 669 (64.1) SBP (mmHg), mean ± SD 128.2 ± 20.8 127.1 ± 20.3 135.1 ± 22.0 < 0.001 DBP (mmHg), mean ± SD 75.1 ± 12.0 74.6 ± 11.9 77.8 ± 12.1 < 0.001 Height (m), mean ± SD 1.6 ± 0.1 1.6 ± 0.1 1.6 ± 0.1 0.139 Weight (kg), mean ± SD 59.0 ± 11.3 58.4 ± 11.1 62.2 ± 12.1 < 0.001 Waist (cm), mean ± SD 84.1 ± 12.4 83.4 ± 12.1 88.4 ± 13.0 < 0.001 BMI (kg/m 2 ), mean ± SD 23.5 ± 3.6 23.3 ± 3.5 24.9 ± 3.8 < 0.001 Night sleep duration (hours), mean ± SD 6.4 ± 1.9 6.4 ± 1.9 6.2 ± 1.9 0.002 BUN (mg/dL), mean ± SD 15.6 ± 4.4 15.7 ± 4.4 15.5 ± 4.4 0.283 FPG (mg/dL), mean ± SD 108.4 ± 32.2 106.0 ± 27.4 122.5 ± 50.0 < 0.001 Scr (mg/dL), mean ± SD 0.8 ± 0.2 0.8 ± 0.2 0.8 ± 0.2 0.004 TC (mg/dL), mean ± SD 193.9 ± 37.8 193.0 ± 37.6 199.1 ± 38.7 < 0.001 TG (mg/dL), Median (IQR) 105.3 (74.3, 154.0) 101.8 (73.5, 148.7) 123.9 (87.6, 185.9) < 0.001 HDL-C (mg/dL), mean ± SD 51.3 ± 15.1 52.0 ± 15.0 47.6 ± 14.8 < 0.001 LDL-C (mg/dL), mean ± SD 117.1 ± 34.6 116.5 ± 34.1 120.9 ± 37.4 < 0.001 hs-CRP (mg/L), Median (IQR) 1.0 (0.5, 2.0) 0.9 (0.5, 1.9) 1.3 (0.7, 2.6) < 0.001 HbA1c (%), mean ± SD 5.2 ± 0.7 5.2 ± 0.6 5.5 ± 1.1 < 0.001 SUA (mg/dL), mean ± SD 4.4 ± 1.2 4.4 ± 1.2 4.5 ± 1.3 < 0.001 Education level, n (%) 0.026 Below primary 3393 (47.1) 2891 (47) 502 (48.1) Primary school 1591 (22.1) 1335 (21.7) 256 (24.5) Middle school 1491 (20.7) 1307 (21.2) 184 (17.6) High school or above 722 (10.0) 621 (10.1) 101 (9.7) Basal heart disease, n (%) 638 ( 8.9) 396 (6.4) 242 (23.2) < 0.001 Basal stroke, n (%) 96 ( 1.3) 52 (0.8) 44 (4.2) < 0.001 Basal hypertension, n (%) 3337 (46.4) 2633 (42.8) 704 (67.5) < 0.001 Basal diabetes, n (%) 870 (12.1) 570 (9.3) 300 (28.8) < 0.001 Drinking status, n (%) < 0.001 Never 4449 (61.8) 3793 (61.6) 656 (62.9) Former 550 ( 7.6) 441 (7.2) 109 (10.5) Current 2198 (30.5) 1920 (31.2) 278 (26.7) Smoking status, n (%) < 0.001 Never 4511 (62.7) 3836 (62.3) 675 (64.7) Former 556 ( 7.7) 444 (7.2) 112 (10.7) Current 2130 (29.6) 1874 (30.5) 256 (24.5) Antidiabetic Medications, n (%) 187 ( 2.6) 100 (1.6) 87 (8.3) < 0.001 Antihypertensive Medications, n (%) 1265 (17.6) 888 (14.4) 377 (36.1) < 0.001 TyG, Mean ± SD 8.7 ± 0.6 8.6 ± 0.6 8.9 ± 0.7 < 0.001 TyG-BMI, Median (IQR) 204.8 ± 38.5 201.7 ± 37.0 223.1 ± 41.9 < 0.001 TyG-WC, mean ± SD 731.1 ± 132.6 721.0 ± 128.2 790.3 ± 142.5 < 0.001 eGDR, mean ± SD 9.1 ± 2.3 9.3 ± 2.2 7.8 ± 2.3 < 0.001 AIP, Median (IQR) 0.3 (0.1, 0.6) 0.3 (0.1, 0.5) 0.4 (0.2, 0.7) < 0.001 METS-IR, mean ± SD 35.6 ± 7.9 34.9 ± 7.5 39.2 ± 8.8 < 0.001 CVAI, mean ± SD 92.7 ± 43.8 89.3 ± 42.8 113.4 ± 43.9 < 0.001 Open in a new tab Abbreviations: CMM, cardiometabolic multimorbidity; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; Scr, serum creatinine; SUA, serum uric acid; BUN, blood urea nitrogen; CRP, C-reactive protein; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin A1c; TyG, triglyceride-glucose; TyG-BMI, TyG-body mass index; TyG-WC, TyG-waist circumference; eGDR, estimated glucose disposal rate; AIP, atherogenic index of plasma; METS-IR, metabolic score for insulin resistance; CVAI, Chinese visceral adiposity index Date are presented as mean ± standard deviation or median (25–75th interquartile range) or n (%) Additionally, we compared the baseline characteristics of participants excluded due to missing data (e.g., incomplete IR surrogate index information, missing CMM follow-up data, and missing covariate data) with those retained in the study (Table S1). Meanwhile, we compared baseline characteristics between participants who completed follow-up (completers, n = 7197) and those lost to follow-up (drop-outs, n = 1550), as shown in Supplementary Table S12 . Associations and dose–response relationships between IR surrogate indexes and CMM risk Table 2 presents the associations between seven IR surrogate indexes and CMM risk. The findings of both continuous and categorical analyses demonstrated significant relationships. The findings of the study indicated that, among Crude model, Model 1, Model 2 and Model 3, eGDR exhibited a negative association with CMM risk, while other IR surrogate indexes (TyG, TyG-BMI, TyG-WC, AIP, METS-IR, and CVAI) demonstrated a positive association with CMM risk. A per SD increase in TyG index corresponded to a 19.1% elevated risk in the fully adjusted model (HR 1.191, 95% CI 1.074–1.32). A dose–response relationship was observed across TyG quartiles ( P for trend = 0.001), with Q4 exhibiting a 44.3% higher risk than Q1 (HR 1.443, 95% CI 1.167–1.782). A per SD increase in TyG-BMI, TyG-WC, AIP, METS-IR, and CVAI corresponded to adjusted HR (95% CI) of 1.006 (1.004–1.008), 1.002 (1.001–1.002), 1.502 (1.248–1.808), 1.028 (1.02–1.035), and 1.006 (1.004–1.008), respectively. Significant trends persisted for TyG-BMI, TyG-WC, AIP, METS-IR, and CVAI in quartile analyses (all P for trend < 0.001). Conversely, higher eGDR levels exhibited a protective effect, a per SD increase associated with 16.6% risk reduction (HR 0.834, 95% CI 0.781–0.891). Furthermore, Q4 of eGDR was associated with a 62% reduction in risk compared to Q1 (HR 0.38, 95% CI 0.263–0.548; P for trend < 0.001). Table 2. Multivariate regression analysis of the associations between seven IR surrogate indexes and cardiometabolic multimorbidity risk Subgroups Incidence rate Crude model Model 1 Model 2 Model 3 HR (95%CI) P value HR (95%CI) P value HR (95%CI) P value HR (95%CI) P value TyG (per SD) 1043 (14.5) 1.804 (1.664–1.956) < 0.001 1.83 (1.684–1.988) < 0.001 1.195 (1.079–1.324) < 0.001 1.191 (1.074–1.32) < 0.001 TyG (quartiles) Q1 146 (8.1) 1(Ref) 1(Ref) 1(Ref) 1(Ref) Q2 214 (11.9) 1.489 (1.206–1.838) < 0.001 1.45 (1.174–1.79) < 0.001 1.225 (0.991–1.516) 0.0609 1.222 (0.988–1.512) 0.0646 Q3 282 (15.7) 2.008 (1.644–2.452) < 0.001 1.906 (1.559–2.331) < 0.001 1.411 (1.148–1.735) 0.0011 1.393 (1.133–1.712) 0.0017 Q4 401 (22.3) 2.979 (2.465–3.6) < 0.001 2.927 (2.418–3.544) < 0.001 1.465 (1.186–1.809) < 0.001 1.443 (1.167–1.782) < 0.001 P for trend < 0.001 < 0.001 < 0.001 0.001 TyG-BMI (per SD) 1043 (14.5) 1.012 (1.011–1.014) < 0.001 1.013 (1.012–1.015) < 0.001 1.007 (1.005–1.008) < 0.001 1.006 (1.004–1.008) < 0.001 TyG-BMI (quartiles) Q1 142 (7.9) 1(Ref) 1(Ref) 1(Ref) 1(Ref) Q2 184 (10.2) 1.302 (1.046–1.621) 0.018 1.394 (1.118–1.736) 0.0031 1.303 (1.045–1.625) 0.0189 1.298 (1.041–1.618) 0.0207 Q3 271 (15.1) 1.956 (1.597–2.396) < 0.001 2.145 (1.745–2.636) < 0.001 1.589 (1.287–1.961) < 0.001 1.573 (1.274–1.941) < 0.001 Q4 446 (24.8) 3.404 (2.818–4.111) < 0.001 3.887 (3.196–4.728) < 0.001 2.186 (1.773–2.696) < 0.001 2.084 (1.686–2.575) < 0.001 P for trend < 0.001 < 0.001 < 0.001 < 0.001 TyG-WC (per SD) 1043 (14.5) 1.004 (1.003–1.004) < 0.001 1.004 (1.003–1.004) < 0.001 1.002 (1.001–1.002) < 0.001 1.002 (1.001–1.002) < 0.001 TyG-WC (quartiles) Q1 137 (7.6) 1(Ref) 1(Ref) 1(Ref) 1(Ref) Q2 173 (9.6) 1.257 (1.005–1.573) 0.0456 1.253 (1.001–1.568) 0.049 1.145 (0.914–1.434) 0.2393 1.132 (0.904–1.419) 0.2791 Q3 288 (16) 2.186 (1.783–2.679) < 0.001 2.13 (1.736–2.613) < 0.001 1.615 (1.311–1.989) < 0.001 1.582 (1.284–1.949) < 0.001 Q4 445 (24.7) 3.518 (2.905–4.26) < 0.001 3.442 (2.835–4.179) < 0.001 1.876 (1.521–2.313) < 0.001 1.798 (1.456–2.221) < 0.001 P for trend < 0.001 < 0.001 < 0.001 < 0.001 eGDR (per SD) 1043 (14.5) 0.755 (0.733–0.777) < 0.001 0.77 (0.748–0.793) < 0.001 0.817 (0.765–0.873) < 0.001 0.834 (0.781–0.891) < 0.001 eGDR (quartiles) Q1 479 (26.6) 1(Ref) 1(Ref) 1(Ref) 1(Ref) Q2 278 (15.4) 0.547 (0.472–0.634) < 0.001 0.554 (0.478–0.644) < 0.001 0.672 (0.568–0.796) < 0.001 0.703 (0.593–0.833) < 0.001 Q3 181 (10.1) 0.337 (0.284–0.4) < 0.001 0.378 (0.318–0.45) < 0.001 0.558 (0.396–0.788) < 0.001 0.591 (0.418–0.835) 0.0028 Q4 105 (5.8) 0.194 (0.157–0.24) < 0.001 0.218 (0.176–0.27) < 0.001 0.36 (0.249–0.518) < 0.001 0.38 (0.263–0.548) < 0.001 P for trend < 0.001 < 0.001 < 0.001 < 0.001 AIP (per SD) 1043 (14.5) 2.638 (2.23–3.12) < 0.001 2.736 (2.304–3.249) < 0.001 1.524 (1.268–1.831) < 0.001 1.502 (1.248–1.808) < 0.001 AIP (quartiles) Q1 170 (9.4) 1(Ref) 1(Ref) 1(Ref) 1(Ref) Q2 208 (11.6) 1.249 (1.02–1.529) 0.0316 1.244 (1.016–1.524) 0.0349 1.135 (0.926–1.392) 0.2234 1.124 (0.916–1.379) 0.2632 Q3 294 (16.3) 1.799 (1.49–2.173) < 0.001 1.77 (1.463–2.14) < 0.001 1.391 (1.146–1.688) < 0.001 1.363 (1.123–1.655) 0.0017 Q4 371 (20.6) 2.365 (1.972–2.835) < 0.001 2.396 (1.995–2.878) < 0.001 1.494 (1.232–1.811) < 0.001 1.468 (1.21–1.781) < 0.001 P for trend < 0.001 < 0.001 < 0.001 < 0.001 METS-IR (per SD) 1043 (14.5) 1.055 (1.048–1.061) < 0.001 1.06 (1.053–1.067) < 0.001 1.03 (1.022–1.038) < 0.001 1.028 (1.02–1.035) < 0.001 METS-IR (quartiles) Q1 149 (8.3) 1(Ref) 1(Ref) 1(Ref) 1(Ref) Q2 174 (9.7) 1.152 (0.925–1.433) 0.206 1.245 (0.999–1.551) 0.0506 1.124 (0.902–1.402) 0.2979 1.108 (0.888–1.382) 0.3632 Q3 282 (15.7) 1.966 (1.612–2.397) < 0.001 2.155 (1.762–2.635) < 0.001 1.611 (1.313–1.977) < 0.001 1.588 (1.294–1.949) < 0.001 Q4 438 (24.3) 3.168 (2.63–3.815) < 0.001 3.577 (2.952–4.334) < 0.001 1.999 (1.63–2.452) < 0.001 1.899 (1.545–2.335) < 0.001 P for trend < 0.001 < 0.001 < 0.001 < 0.001 CVAI (per SD) 1043 (14.5) 1.013 (1.012–1.015) < 0.001 1.012 (1.011–1.014) < 0.001 1.006 (1.005–1.008) < 0.001 1.006 (1.004–1.008) < 0.001 CVAI (quartiles) Q1 123 (6.8) 1(Ref) 1(Ref) 1(Ref) 1(Ref) Q2 179 (9.9) 1.455 (1.157–1.831) 0.0014 1.362 (1.081–1.716) 0.0087 1.252 (0.992–1.58) 0.0581 1.249 (0.99–1.575) 0.0611 Q3 270 (15) 2.281 (1.843–2.823) < 0.001 2.05 (1.65–2.547) < 0.001 1.63 (1.306–2.034) < 0.001 1.606 (1.287–2.005) < 0.001 Q4 471 (26.2) 4.196 (3.441–5.118) < 0.001 3.587 (2.921–4.403) < 0.001 2.174 (1.752–2.696) < 0.001 2.078 (1.672–2.583) < 0.001 P for trend < 0.001 < 0.001 < 0.001 < 0.001 Open in a new tab Model 1 adjusted for age, gender, marital status, residence, educational level, smoking status, and drinking status Model 2 for variables in Model 1 and SBP, DBP, TC, Scr, SUA, BUN, hs-CRP, HbA1c, night sleep duration, basal heart disease, basal stroke, basal hypertension, and basal diabetes Model 3 for variables in Model 2 and antidiabetic medications, and antihypertensive medications Abbreviations: CMM, cardiometabolic multimorbidity; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; Scr, serum creatinine; SUA, serum uric acid; BUN, blood urea nitrogen; CRP, C-reactive protein; HbA1c, glycosylated hemoglobin A1c; TyG, triglyceride-glucose; TyG-BMI, TyG-body mass index; TyG-WC, TyG-waist circumference; eGDR, estimated glucose disposal rate; AIP, atherogenic index of plasma; METS-IR, metabolic score for insulin resistance; CVAI, Chinese visceral adiposity index. HR, hazard ratio; CI, confidence interval; Ref, reference Restricted cubic spline analyses revealed distinct associations between seven IR surrogates and CMM risk. After confounding factors adjustment, TyG-BMI, TyG-WC, METS-IR, and eGDR demonstrated significant nonlinear associations with incident new-onset CMM ( P -nonlinearity < 0.05; Fig. 2 B, C, E, G), while TyG, AIP, and CVAI exhibited linear positive associations ( P -nonlinearity > 0.05; Fig. 2 A, D, F). These findings robustly aligned with multivariable regression results. Specifically, eGDR showed a L-shaped association ( P -nonlinearity < 0.05), with CMM risk decreasing until the inflection point at 11.82 (HR = 0.758; 95% CI: 0.702–0.818) and stabilizing thereafter ( P > 0.05) (Table S5). Conversely, TyG-WC displayed a U-shaped relationship (inflection point: 572.14), demonstrating risk reduction below (HR = 0.997; 95% CI: 0.995–0.999) and elevations above this threshold (HR = 1.002; 95% CI: 1.002–1.003; both P < 0.05) (Table S3). Notably, TyG-BMI and METS-IR both showed reverse L-shaped patterns with inflection points at 248.09 (HR = 1.009; 95% CI: 1.006–1.012) (Table S2) and 45.16 (HR = 1.044; 95% CI: 1.030–1.058) (Table S4) respectively. Below these thresholds, CMM risk progressively increased before stabilizing (Tables 3 and 4 ). Fig. 2. Open in a new tab Restricted cubic spline curves for CMM by TyG ( A ), TyG-BMI ( B ), TyG-WC ( C ), AIP ( D ), METS-IR ( E ), CVAI ( F ) and eGDR ( G ) after covariate adjustment. Heavy central line represents the estimated adjusted hazard ratio, with shaded ribbons denoting 95% confidence interval. The model is adjusted for age, gender, marital status, residence, educational level, smoking status, drinking status, SBP, DBP, TC, Scr, SUA, BUN, hs-CRP, HbA1c, night sleep duration, basal heart disease, basal stroke, basal hypertension, basal diabetes, antidiabetic medications, and antihypertensive medications. Abbreviations: CMM, cardiometabolic multimorbidity; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; Scr, serum creatinine; SUA, serum uric acid; BUN, blood urea nitrogen; CRP, C-reactive protein; HbA1c, glycosylated hemoglobin A1c; TyG, triglyceride-glucose; TyG-BMI, TyG-body mass index; TyG-WC, TyG-waist circumference; eGDR, estimated glucose disposal rate; AIP, atherogenic index of plasma; METS-IR, metabolic score for insulin resistance; CVAI, Chinese visceral adiposity index. HR, hazard ratio; CI, confidence interval; Ref, reference Table 3. Incremental predictive value of seven insulin resistance surrogate indexes for CMM Model NRI(95% CI) P value IDI(95% CI) P value Basic model Ref Ref Ref Ref + TyG 0.042 (0.005–0.071) 0.016 0.001 (-0.002–0.006) 0.511 + TyG-BMI 0.087 (0.049–0.124) 0.0 0.006 (0.001–0.012) 0.012 + TyG-WC 0.095 (0.054–0.136) 0.0 0.005 (0.001–0.011) 0.012 + AIP 0.044 (0.013–0.089) 0.008 0.003 (0.0–0.006) 0.02 + eGDR 0.084 (0.034–0.125) 0.008 0.005 (0.003–0.008) 0.0 + METS-IR 0.106 (0.067–0.145) 0.0 0.006 (0.002–0.012) 0.008 + CVAI 0.098 (0.059–0.138) 0.0 0.007 (0.002–0.013) 0.0 Open in a new tab The basic model included age, gender, marital status, residence, educational level, smoking status, drinking status, SBP, DBP, TC, Scr, SUA, BUN, hs-CRP, HbA1c, night sleep duration, basal heart disease, basal stroke, basal hypertension, basal diabetes, antidiabetic medications, and antihypertensive medications Abbreviations: NRI, net reclassification improvement; Ref, reference; IDI, integrated discrimination improvement; CI, confidence interval; Ref, reference; CMM, cardiometabolic multimorbidity; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; Scr, serum creatinine; SUA, serum uric acid; BUN, blood urea nitrogen; CRP, C-reactive protein; HbA1c, glycosylated hemoglobin A1c; TyG, triglyceride-glucose; TyG-BMI, TyG-body mass index; TyG-WC, TyG-waist circumference; eGDR, estimated glucose disposal rate; AIP, atherogenic index of plasma; METS-IR, metabolic score for insulin resistance; CVAI, Chinese visceral adiposity index Table 4. The time-dependent AUC values at clinically relevant follow-up intervals Times (month) Variable AUC (95% CI) 24 TyG 0.604 (0.518–0.690) TyG-BMI 0.672 (0.591–0.753) TyG-WC 0.724 (0.649–0.799) AIP 0.583 (0.500–0.666) CVAI 0.698 (0.617–0.778) METS-IR 0.679 (0.600–0.759) eGDR 0.754 (0.687–0.821) 48 TyG 0.622 (0.587–0.658) TyG-BMI 0.664 (0.630–0.698) TyG-WC 0.696 (0.664–0.728) AIP 0.618 (0.583–0.653) CVAI 0.700 (0.668–0.732) METS-IR 0.668 (0.634–0.702) eGDR 0.710 (0.679–0.741) 84 TyG 0.629 (0.608–0.651) TyG-BMI 0.659 (0.638–0.681) TyG-WC 0.671 (0.650–0.693) AIP 0.614 (0.592–0.636) CVAI 0.680 (0.659–0.701) METS-IR 0.661 (0.639–0.683) eGDR 0.702 (0.682–0.722) 108 TyG 0.629 (0.607–0.651) TyG-BMI 0.630 (0.608–0.652) TyG-WC 0.638 (0.616–0.660) AIP 0.607 (0.585–0.629) CVAI 0.646 (0.625–0.668) METS-IR 0.631 (0.609–0.653) eGDR 0.680 (0.658– 0.701) Open in a new tab AUC, the area under the curve; CI, confidence interval; TyG, triglyceride-glucose; TyG-BMI, TyG-body mass index; TyG-WC, TyG-waist circumference; eGDR, estimated glucose disposal rate; AIP, atherogenic index of plasma; METS-IR, metabolic score for insulin resistance; CVAI, Chinese visceral adiposity index Kaplan–Meier survival curves of the IR surrogate indexes for the cumulative CMM As shown in Fig. 3 , the Kaplan–Meier curves illustrate that there is a progressive increase in the cumulative incidence of CMM across quartiles of all positive-associated indexes (TyG: log-rank P = 0.003; TyG-BMI, TyG-WC, AIP, METS-IR, CVAI: all log-rank P < 0.001). In contrast, the eGDR demonstrated an inverse correlation, exhibiting a substantial decline in incidence from Q1 to Q4 (log-rank P < 0.001) (Fig. 4 ). Fig. 3. Open in a new tab Kaplan–Meier curves for CMM risk stratified by TyG ( A ), TyG-BMI ( B ), TyG-WC ( C ), AIP ( D ), METS-IR ( E ), CVAI ( F ) and eGDR ( G ) after covariate adjustment. The model is adjusted for age, gender, marital status, residence, educational level, smoking status, drinking status, SBP, DBP, TC, Scr, SUA, BUN, hs-CRP, HbA1c, night sleep duration, basal heart disease, basal stroke, basal hypertension, basal diabetes, antidiabetic medications, and antihypertensive medications. Abbreviations: CMM, cardiometabolic multimorbidity; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; Scr, serum creatinine; SUA, serum uric acid; BUN, blood urea nitrogen; CRP, C-reactive protein; HbA1c, glycosylated hemoglobin A1c; TyG, triglyceride-glucose; TyG-BMI, TyG-body mass index; TyG-WC, TyG-waist circumference; eGDR, estimated glucose disposal rate; AIP, atherogenic index of plasma; METS-IR, metabolic score for insulin resistance; CVAI, Chinese visceral adiposity index Fig. 4. Open in a new tab Subgroup analysis of the association among TyG ( A ), TyG-BMI ( B ), TyG-WC ( C ), AIP ( D ), CVAI ( E ), METS-IR ( F ), eGDR ( G ) and CMM. The model is adjusted for age, gender, marital status, residence, educational level, smoking status, drinking status, SBP, DBP, TC, Scr, SUA, BUN, hs-CRP, HbA1c, night sleep duration, basal heart disease, basal stroke, basal hypertension, basal diabetes, antidiabetic medications, and antihypertensive medications. Abbreviations: CMM, cardiometabolic multimorbidity; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; Scr, serum creatinine; SUA, serum uric acid; BUN, blood urea nitrogen; CRP, C-reactive protein; HbA1c, glycosylated hemoglobin A1c; TyG, triglyceride-glucose; TyG-BMI, TyG-body mass index; TyG-WC, TyG-waist circumference; eGDR, estimated glucose disposal rate; AIP, atherogenic index of plasma; METS-IR, metabolic score for insulin resistance; CVAI, Chinese visceral adiposity index Subgroup analyses to examine the associations between IR surrogate indexes and CMM risk Bonferroni correction was applied to interaction tests to account for multiple comparisons, adjusting the significance threshold according to the number of tests per IR surrogate index. Post-correction analysis revealed no statistically significant interaction across any predefined subgroups. This consistency indicates robust associations between IR surrogates and CMM risk independent of baseline demographic or clinical characteristics. Predictive performance of IR surrogate indexes for CMM risk Table 5 and Fig. 6 demonstrate the comparative predictive capabilities of seven IR surrogate indexes for CMM risk. The eGDR index consistently demonstrated superior discriminative ability, achieving the highest AUC (0.688; 95% CI 0.671–0.706) among all IR surrogate indexes, followed sequentially by CVAI, TyG-WC, TyG-BMI, METS-IR, TyG, and AIP. CVAI exhibited the highest sensitivity (0.66), whereas eGDR showed optimal specificity (0.70). Optimal cutoff values were: eGDR (7.64), CVAI (97.77), TyG-WC (765.31), TyG-BMI (210.01), METS-IR (36.25), TyG (8.65), and AIP (0.40). Table 5. ROC curves of seven IR surrogates and CMM risk at month in the overall population Variable AUC (95% CI) P value P for comparison Optimal cutoff value sensitivity specificity Youden’s index eGDR 0.688 (0.671, 0.706) < 0.001 reference 7.64 0.57 0.70 0.27 TyG 0.628 (0.609, 0.646) < 0.001 < 0.001 8.65 0.62 0.57 0.19 TyG-BMI 0.652 (0.634, 0.670) < 0.001 < 0.001 210.01 0.61 0.63 0.24 TyG-WC 0.662 (0.643, 0.680) < 0.001 < 0.001 765.31 0.60 0.66 0.26 AIP 0.607 (0.589, 0.625) < 0.001 < 0.001 0.40 0.55 0.62 0.17 CVAI 0.670 (0.652, 0.688) < 0.001 0.012 97.77 0.66 0.60 0.26 METS-IR 0.651 (0.633, 0.670) < 0.001 < 0.001 36.25 0.62 0.63 0.25 Open in a new tab AUC, the area under the curve; CI, confidence interval; TyG, triglyceride-glucose; TyG-BMI, TyG-body mass index; TyG-WC, TyG-waist circumference; eGDR, estimated glucose disposal rate; AIP, atherogenic index of plasma; METS-IR, metabolic score for insulin resistance; CVAI, Chinese visceral adiposity index Fig. 6. Open in a new tab ROC curves of seven IR surrogates and CMM risk. AUC, area under the curve; CMM, cardiometabolic multimorbidity; TyG, triglyceride-glucose; TyG-BMI, TyG-body mass index; TyG-WC, TyG-waist circumference; eGDR, estimated glucose disposal rate; AIP, atherogenic index of plasma; METS-IR, metabolic score for insulin resistance; CVAI, Chinese visceral adiposity index Time-dependent ROC analysis revealed significant temporal variation in predictive performance (Figs. S3 , 5 , Table 4 ). eGDR maintained superior discriminative capacity across all intervals, with peak AUC at 24 months (0.754; 95% CI 0.687–0.821) followed by sequential declines at 48 (0.710; 0.679–0.741), 84 (0.702; 0.682–0.722), and 108 months (0.680; 0.658–0.701). TyG-WC demonstrated secondary superiority at 24 months (AUC = 0.724; 0.649–0.799) but exhibited accelerated performance decay. Conversely, CVAI showed progressive relative improvement, surpassing other indices (except eGDR) at subsequent intervals. AIP consistently yielded the lowest discriminative capacity across all timepoints (24 month: 0.583; 0.500–0.666; 108 month: 0.607; 0.585–0.629). All indices maintained AUC values > 0.6, confirming their predictive utility (Fig. 6 ). Fig. 5. Open in a new tab Time-dependent predictive capacity of TyG, TyG-BMI, TyG-WC, AIP, METS-IR, CVAI and eGDR for CMM. AUC, area under the curve; CMM, cardiometabolic multimorbidity; TyG, triglyceride-glucose; TyG-BMI, TyG-body mass index; TyG-WC, TyG-waist circumference; eGDR, estimated glucose disposal rate; AIP, atherogenic index of plasma; METS-IR, metabolic score for insulin resistance; CVAI, Chinese visceral adiposity index Bootstrap internal validation (1000 replicates) produced optimism-corrected AUC estimates closely aligned with original values (Table S12), indicating minimal evidence of overfitting. Calibration slopes approaching unity and favourable Brier scores collectively supported adequate model fit, confirming stable predictive performance across all indexes (Fig. S4 and Table S13 ). Table 3 quantifies incremental value via IDI and NRI metrics. METS-IR achieved the highest NRI (0.106; 95% CI 0.067–0.145; P < 0.001), followed by CVAI (0.098; 0.059–0.138; P < 0.001), TyG-WC (0.095; 0.054–0.136; P < 0.001), TyG-BMI (0.087; 0.049–0.124; P < 0.001), and eGDR (0.084; 0.034–0.125; P = 0.008). All indexes except TyG demonstrated statistically significant IDI improvements ( P < 0.05), with CVAI showing the greatest discrimination enhancement (IDI = 0.007; 0.002–0.013; P < 0.001). Notably, TyG significantly improved reclassification (NRI = 0.042; P = 0.016) but not discrimination (IDI = 0.001; P = 0.511) [ 20 ]. Sensitivity analyses To assess the robustness of the key findings, we performed a series of sensitivity analyses. First, consistent results were observed in both the complete-case analysis and the multiple imputation datasets (Supplementary Table S4 ). Second, after excluding individuals with diabetes, heart disease, or stroke at baseline, the seven IR surrogate indexes retained a significant association with CMM during follow-up (Supplementary Table S5 ). Third, we observed similar associations after accounting for the competing risks of mortality (Supplementary Fig. S2 , Table S6 ). Fourth, Incorporating this physical activity measure yielded no significant differences in the multivariable-adjusted associations between insulin resistance surrogates and CMM risk compared to the primary model (Supplementary Table S7 ). Discussion CMM poses a substantial public health burden, adversely impacting individual wellbeing and healthcare systems [ 40 , 41 ]. IR has been identified as a pivotal pathophysiological driver of CMM development. The identification of practical biomarkers for the early risk stratification of CMM is therefore of paramount importance from a clinical perspective. IR manifests clinically through key features such as hyperglycaemia, dyslipidaemia, hypertension, and obesity [ 42 ]. To quantify IR severity pragmatically, several surrogate markers (eGDR, CVAI, TyG, TyG-BMI, TyG-WC, METS-IR, AIP) have emerged, each integrating distinct combinations of these cardio-metabolic derangements. To the best of our knowledge, this study explored the association between seven surrogate IR indexes and the risk of CMM using a nationally representative cohort from China. Our findings revealed a considerable association between the seven surrogate IR indexes and an increased risk of new‐onset CMM in middle‐aged and older Chinese populations. In order to explore the stability of seven IR surrogate indexes and CMM risk in different groups, subgroup and interaction analyses were performed. These analyses included subgroups categorized by age (45–59 years, and ≥ 60 years), gender (male, female), marital status, drinking status and smoking status, BMI (< 24 kg/m 2 , ≥ 24 kg/m 2 ) and hypertension. It revealed no statistically significant interaction across any subgroups. This consistency indicates robustness associations between IR surrogates and CMM risk independent of baseline demographic or clinical characteristics, which suggests that the findings of the present study may be applicable to the general population [ 23 , 31 , 32 ]. However, in participants with a normal BMI, each SD increase in TyG, AIP and eGDR showed more associations with incident CMM than the elevated BMI status. Multiple observational studies demonstrate that more than 30% of individuals with normal body weight exhibit metabolic disorders. Compared to their metabolically healthy counterparts at the same weight, those with such abnormalities are observed to be at significantly higher risk for developing metabolic and cardiovascular diseases [ 43 , 44 ]. Furthermore, normal-weight individuals may lack consistent health monitoring or preventive measures against cardiometabolic disease. Early identification of metabolic abnormalities in this population is therefore critical, particularly among Chinese individuals who demonstrate heightened susceptibility to visceral adiposity and insulin resistance despite lower absolute BMI values than Western populations [ 45 ]. Nevertheless, data on preventing metabolic abnormalities in normal-weight Chinese cohorts remain limited. Tian et al. identified a significant association between longitudinal TyG index trajectories and subsequent cardiovascular disease risk in normal-weight individuals [ 21 ]. Aligning with this evidence, our findings substantiate the pivotal role of seven IR surrogate indexes in predicting CMM among normal-weight individuals. We employed RCS to investigate dose–response relationships between IR surrogate indexes and CMM risk. After confounding factors adjustment, TyG-BMI, TyG-WC, METS-IR, and eGDR demonstrated significant nonlinear associations with incident new-onset CMM. Specifically, an L-shaped association was identified between eGDR and CMM risk, characterised by a threshold at 11.82. These results suggest an inverse association between eGDR and CMM risk below this threshold, nevertheless, the protective effect plateaued at higher levels, implying diminishing returns. Maintenance of eGDR above this threshold may attenuate CMM risk. Accordingly, individuals with subthreshold eGDR levels merit clinical surveillance. These observations corroborate recent literature [ 22 , 46 , 47 ]. Huo et al. reported a comparable L-shaped association (threshold 10.53) between eGDR and stroke risk on the general population aged ≥ 45 years in China [ 22 ]. Similarly, Liang et al. detected specific eGDR inflection points across glycaemic strata (11.77, 11.15, and 11.56 for normoglycaemia, prediabetes, and diabetes, respectively), with each unit increment below these thresholds conferring analogous cardiovascular protective effects [ 46 ]. By contrast, Jiang et al. demonstrated a linear inverse correlation between eGDR and stroke risk [ 20 ]. Discrepancies with prior studies may stem from population heterogeneity, pathophysiological complexity of CMM, RCS parameter specifications, and analytical model differences. While the precise mechanisms linking eGDR to CMM require further elucidation, several pathophysiological pathways likely underlie this association. Primarily, eGDR represents a validated proxy for IR, a key driver of systemic atherosclerosis and ensuing cardiometabolic complications. Secondly, IR compromises nitric oxide bioavailability, impairing endothelial function and inducing vascular injury, which accelerates progressive end-organ damage relevant to CMM. Thirdly, IR promotes chronic inflammation, coagulopathy, and oxidative stress—processes which collectively instigate vascular compromise and metabolic dysregulation, thereby heightening vulnerability to clustered cardiometabolic disorders. Finally, IR frequently coexists with hypertension, dysglycaemia, and adiposity—a pathophysiological triad constituting metabolic syndrome that synergistically propels CMM pathogenesis. Thus, reduced eGDR may signify accumulating subclinical vascular and end-organ injury preceding manifest CMM. Clinically, maintaining eGDR within optimal thresholds represents a key modifiable factor for attenuating CMM risk [ 22 ]. For TyG-WC, a U-shaped association was observed, with a nadir at 572.14. Below this cut-off, each unit increment was associated with reduced CMM risk (HR 0.997, 95% CI 0.995–0.999). Conversely, above this nadir, each additional unit significantly elevated risk (HR 1.002, 95% CI 1.002–1.003). Both gradients attained statistical significance ( P < 0.05). This U-shaped pattern likely reflects distinct pathophysiological mechanisms at both extremes. Elevated TyG-WC values indicate insulin resistance and metabolic dysregulation, culminating in lipotoxicity, oxidative stress, chronic inflammation, and atherosclerosis, thereby augmenting cardiovascular and multi-organ failure risk. Conversely, suboptimal levels may signify malnutrition, metabolic insufficiency, or chronic wasting syndromes, implying compromised immune competence or severe comorbidities. Consequently, maintenance of TyG-WC within this optimal range is imperative for prognostic optimisation in CMM patients [ 27 ]. Given the nonlinear relationships identified between IR surrogates and CMM risk, clinical risk assessment should incorporate population-specific characteristics and individualised surrogate thresholds for enhanced precision. The findings of this prospective cohort study provide novel evidence on temporal dynamics and comparative utility of seven IR surrogate indexes for predicting CMM in Chinese middle-aged and older adults. Previous research by Xiao et al. demonstrated associations between IR surrogates and incident CMM in middle-aged and older Chinese adults, with CVAI identified as having superior predictive capability [ 23 ]. However, that study was limited by the absence of time-to-event analyses. Similarly, Jiang et al. reported significant associations between six IR surrogate indices and elevated stroke risk in dysglycaemic populations. In that study, eGDR was identified as a particularly promising predictor for stroke risk relative to CVAI in middle-aged and elderly Chinese cohorts [ 20 ]. Given the protracted developmental course of CMM, time-dependent AUC analysis was employed to assess the predictive performance of IR surrogate indexes across multiple follow-up intervals. The time-dependent AUC analysis revealed that all the predictive models demonstrated moderate discrimination, with values ranging between 0.60 and 0.75. According to established diagnostic standards, AUC values below 0.60 indicate poor discrimination, 0.60–0.75 reflect moderate discrimination, and exceeding 0.75 denotes good discrimination. Thus, the observed performance falls within the moderate range. In the present study, eGDR exhibited superior predictive discrimination (AUC 0.68–0.76) during early follow-up and across all intervals. Owing to its computational simplicity, this metric may be recommended for public health screening programmes. TyG-WC evinced suboptimal yet notable performance at 24 months; however, rapid deterioration in discriminative accuracy was observed thereafter. By contrast, CVAI manifested progressively improved discrimination during extended follow-up, eventually outperforming other indices (except eGDR) at later timepoints. The sustained predictive accuracy of eGDR establishes this index as a feasible tool for primary care screening. The optimal cut-off value for eGDR was determined to be 7.64. Clinically, ROC-derived cut-offs (eGDR < 7.64) are utilised for identifying high-risk individuals requiring intervention. Conversely, spline-derived thresholds (eGDR = 11.82) serve as therapeutic targets for risk reduction. Together, these complementary approaches constitute a two-tiered framework for CMM management. Notably, integrated discrimination and net reclassification analyses indicated that METS-IR, CVAI, TyG-WC, TyG-BMI, and eGDR significantly enhanced the predictive performance of the baseline model (all IDI and NRI P < 0.01). By contrast, TyG exhibited limited incremental predictive value. This study possesses several notable strengths. First, it utilized a prospective cohort design with data from CHARLS, featuring an extensive cohort and follow-up extending to 9 years. Second, the investigation assessed the predictive performance of seven surrogate IR indexes for CMM risk among middle-aged and older Chinese adults, offering novel perspectives for early CMM risk identification and prevention. Third, rigorous adjustment for potential confounders was undertaken. Additionally, comprehensive subgroup analyses were conducted to enhance the reliability and robustness of the findings. Fourth, Fine-Gray subdistribution hazards models were employed to account for competing events (e.g., non-CMM mortality), thereby ensuring unbiased effect estimates. Fifth, time-dependent ROC curve analysis was utilised to evaluate the predictive discrimination of each index. Clinically applicable thresholds were subsequently established through Youden's index optimisation, with incremental predictive value quantified via NRI and IDI. Finally, non-linear dose–response relationships were modelled using RCS, enabling identification of critical thresholds. Nevertheless, this study possesses several limitations. First, as an observational investigation, it cannot establish causality between the seven surrogate IR indexes and CMM, and the findings are therefore limited to an association interpretation. Second, despite rigorous adjustment for multiple known confounders, residual confounding may persist from unmeasured factors such as genetics, dietary patterns, and environmental exposures. Third, our study relied on surrogate indexes rather than the hyperinsulinemic-euglycemic clamp (HEC) to assess insulin resistance. While these surrogates are pragmatic for large cohorts and have demonstrated reasonable correlation with HEC in prior validation studies, they remain indirect proxies. Thus, our findings should be interpreted as identifying clinically useful predictors of CMM risk, not as definitive measures of insulin resistance per se. Future studies in cohorts with HEC data—such as the China Kadoorie Biobank (CKB), UK Biobank, or MESA—are needed to validate the relationship between clamp-measured IR and CMM risk and to confirm the performance of the surrogates highlighted here. Fouth, as the CHARLS database primarily recruited middle-aged and older adults from the Chinese population, the ethnic and regional specificity of the cohort limits the extrapolation of findings to other ethnicities and age groups. However, the cohort’s representativeness for its target demographic ensures high internal validity for applications in middle-aged and older adults from the Chinese population—a group facing a significant and growing burden of CMM. Future studies should validate our findings in younger cohorts, ethnically diverse populations, and institutionalised settings to broaden generalisability. Conclusion In conclusion, our findings indicate that the seven IR surrogate indexes are strongly associated with CMM occurrence in middle-aged and elderly Chinese populations. Among these, eGDR demonstrates strong potential for assessing CMM risk. Crucially, eGDR is readily calculable without complex equipment, making it a simple and effective clinical screening tool to identify individuals at higher CMM risk. This facilitates the implementation of early intervention and prevention strategies. Further research utilising intervention strategies and encompassing diverse multi-ethnic cohorts is required to validate our findings. Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 (552.4KB, docx) Acknowledgements We gratefully acknowledge all participants and CHARLS Research Group members for their essential contributions. We sincerely thank Dr Jie Liu, PhD, Department of Vascular and Endovascular Surgery, PLA General Hospital, China, for his valuable feedback and suggestions on the manuscript. Abbreviations CMM Cardiometabolic multimorbidity CHARLS Chinese health and retirement longitudinal study CMDs Cardiometabolic diseases IR Insulin resistance HOMA-IR Homeostatic model assessment of insulin resistance TyG Triglyceride-glucose index eGDR Estimated glucose disposal rate CVAI Chinese visceral adiposity index METS-IR Metabolic score for IR AIP Atherogenic index of plasma FPG Fasting plasma glucose WC Waist circumference BMI Body mass index SBP Systolic blood pressure DBP Diastolic blood pressure HDL-C Highdensity lipoprotein cholesterol LDL-C Lowdensity lipoprotein cholesterol TC Total cholesterol TG Triglyceride HbA1c Glycosylated hemoglobin A1c Scr Serum creatinine SUA Serum uric acid BUN Blood urea nitrogen hs-CRP High-sensitivity C-reactive protein SD Standard deviation IQR Interquartile range HR Hazard ratio CI Confidence interval RCS Restrictedcubic spline K-M Kaplan–Meier curve ROC Receiveroperating characteristic AUC Area under the curve Author contributions Jun Lai and Zongyan Liu wrote and edited the manuscript, Jun Lai and Xiaoqing Kong performed statistical analysis, Huajie Wang and Yufeng Wei helped collect and analyze data, Yongxiao Cao supervised data collection, and rigorously reviewed the manuscript for important scientific content. All authors read and approved the final manuscript. Funding Supported by the funds for the Training Object of Medical Academic Leader of Ganzhou City. Data availability No datasets were generated or analysed during the current study. Declarations Ethics approval and consent to participate The CHARLS was conducted in compliance with the Declaration of Helsinki and approved by the Peking University Institutional Review Board (IRB00001052-11015). All participants provided written informed consent prior to enrollment. Competing interests The authors declare no competing interests. Footnotes Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Contributor Information Jun Lai, Email: [email protected]. Yongxiao Cao, Email: [email protected]. References 1. Freisling H, Viallon V, Lennon H, Bagnardi V, Ricci C, Butterworth AS, et al. 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