Metabolic Status Modifies the Predictive Value of the C-reactive Protein– Triglyceride–Glucose Index–Waist-to-Height Ratio for Major Adverse Cardiovascular and Cerebrovascular Events: A Prospective Cohort Study from CHARLS

Metabolic Status Modifies the Predictive Value of the C-reactive Protein– Triglyceride–Glucose Index–Waist-to-Height Ratio for Major Adverse Cardiovascular and Cerebrovascular Events: A Prospective Cohort Study from CHARLS | 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 Metabolic Status Modifies the Predictive Value of the C-reactive Protein– Triglyceride–Glucose Index–Waist-to-Height Ratio for Major Adverse Cardiovascular and Cerebrovascular Events: A Prospective Cohort Study from CHARLS Xuanzhe Li, Tingmin Li, Yitong Meng, Lishuang Zhang, Gen Yan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8922730/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Apr, 2026 Read the published version in Cardiovascular Diabetology → Version 1 posted 19 You are reading this latest preprint version Abstract Background The C-reactive protein–triglyceride–glucose index combined with the waist-to-height ratio (CTI-WHtR) is a novel composite biomarker integrating inflammation, insulin resistance, and central obesity. Whether its predictive value for major adverse cardiovascular and cerebrovascular events (MACCE) varies across metabolic states remains unknown. Methods We included 6,993 participants free of cardiovascular disease at baseline from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2020). Participants were classified into normal glucose tolerance (NGT, n = 2,916), prediabetes (n = 3,106), and type 2 diabetes (T2D, n = 971). Stratified multivariable Cox regression with stepwise covariate adjustment, multiplicative interaction testing, head-to-head comparison of eight metabolic indices, restricted cubic spline (RCS) dose–response analyses, subgroup analyses, and multiple sensitivity analyses were performed. Results Over a mean follow-up of 8.0 ± 2.0 years, 1,467 MACCE events occurred. After full adjustment including BMI, each standard deviation increase in CTI-WHtR was significantly associated with MACCE in the NGT group (HR 1.26, 95% CI 1.11–1.43, P = 0.0003), but not in the prediabetes (HR 1.04, P = 0.43) or T2D group (HR 1.12, P = 0.17). Pairwise interaction testing confirmed effect modification (prediabetes vs. NGT P = 0.027 for MACCE). RCS analyses revealed that the dose–response relationship was consistently linear in the NGT group (all P non−linearity > 0.25), whereas it shifted to a non-linear inverted-U shape in the T2D group (P non−linearity = 0.017 for MACCE). In head-to-head comparisons, CTI-based indices achieved higher C-indices in the NGT group, while TyG-based indices performed better in the T2D group, demonstrating a “group-switching” phenomenon. Conclusions The predictive value of CTI-WHtR for MACCE is modified by metabolic status in both magnitude and functional form. CTI-WHtR demonstrates robust, linear, and independent predictive value in normoglycemic individuals, while the dose–response relationship transforms to an inverted-U shape in type 2 diabetes. These findings establish that metabolic status should be considered when interpreting both the strength and functional form of metabolic index–cardiovascular risk associations. C-reactive protein–triglyceride–glucose index waist-to-height ratio major adverse cardiovascular and cerebrovascular events metabolic status insulin resistance predictive paradox dose–response nonlinearity prospective cohort CHARLS Figures Figure 1 Figure 2 Figure 3 Figure 4 Research Insights What is currently known about this topic? The CTI-WHtR is a recently proposed composite biomarker integrating inflammation, insulin resistance, and central obesity. Prior studies have demonstrated associations between CTI derivatives and cardiovascular events, but whether metabolic status modifies the predictive value of CTI-WHtR for MACCE has not been investigated. What is the key research question? Does glycemic status (normal glucose tolerance, prediabetes, and type 2 diabetes) modify the magnitude and functional form of the CTI-WHtR–MACCE association? What is new? CTI-WHtR demonstrates robust, linear, and independent predictive value for MACCE specifically in normoglycemic individuals. In type 2 diabetes, the dose–response relationship undergoes a qualitative transformation from linear to inverted-U-shaped. A “group-switching” phenomenon was observed, with CTI-based indices outperforming TyG-based indices in the NGT group and vice versa in the T2D group. How might this study influence clinical practice? These findings suggest that metabolic-status-specific index selection (CTI-based for normoglycemic populations, TyG-based for diabetic populations) should guide cardiovascular risk stratification. Non-linear dose–response modeling should be routinely employed when evaluating metabolic indices across glycemic strata. Background Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, accounting for approximately 17.9 million deaths annually.[ 1 ] China bears a disproportionate burden, with CVD responsible for over 40% of all deaths and its prevalence continuing to rise alongside rapid urbanization, dietary transitions, and population aging.[ 2 ] Major adverse cardiovascular and cerebrovascular events (MACCE)—a composite endpoint encompassing incident heart disease, stroke, and all-cause death—serve as the standard measure of cardiovascular burden in epidemiological research.[ 3 , 4 ] Early identification of individuals at elevated MACCE risk using accessible and cost-effective biomarkers is therefore a public health priority. Composite biomarkers that integrate multiple pathophysiological pathways have emerged as promising tools for this purpose. Insulin resistance (IR) has been increasingly recognized as a central mechanism linking metabolic dysfunction to atherosclerotic cardiovascular disease.[ 5 ] The triglyceride-glucose (TyG) index, calculated as Ln[TG (mg/dL) × FPG (mg/dL)/2], was among the first surrogate IR markers validated for cardiovascular risk prediction.[ 6 ] An umbrella review of meta-analyses confirmed robust associations between TyG and a wide spectrum of cardiovascular outcomes.[ 7 , 8 ] Subsequently, anthropometric-modified variants—TyG-BMI, TyG-waist circumference (TyG-WC), and TyG-waist-to-height ratio (TyG-WHtR)—were developed to incorporate obesity dimensions and demonstrated incremental predictive value in both Chinese and Western populations.[ 9 , 10 ] More recently, the C-reactive protein–triglyceride–glucose index (CTI), formulated as 0.412 × Ln(CRP) + [Ln(TG × FPG)] / 2, was proposed to integrate systemic inflammation with IR, and has shown associations with stroke, coronary heart disease, cardiovascular events, and mortality in both Chinese and international cohorts.[ 11 – 17 ] Its further combination with WHtR, yielding the CTI-WHtR composite, was designed to simultaneously capture inflammation, IR, and central adiposity, and has recently demonstrated strong predictive value for stroke in a Chinese population.[ 18 ] A critical but understudied question is whether the predictive efficacy of these composite indices varies across different metabolic states. Glycemic status represents a spectrum from normal glucose tolerance (NGT) through prediabetes to overt type 2 diabetes (T2D), each stage characterized by progressively worsening IR, inflammation, and metabolic dysregulation.[ 19 ] A recent meta-analysis of 50 cohorts involving over 7.2 million participants by Zhang et al. found that diabetes status modified the association between the TyG index and ischemic heart disease as well as all-cause mortality, with stronger associations observed in non-diabetic populations.[ 20 ] Xu et al. similarly reported that diabetes modified TyG-MACCE associations in patients with coronary heart disease.[ 21 ] However, whether metabolic status similarly modifies the predictive value of CTI-WHtR for MACCE has not been investigated. Huo et al. reported that the association between CTI and stroke risk differed across glycemic strata using CHARLS data, but did not examine CTI-WHtR or MACCE as the composite endpoint.[ 11 ] To address this gap, we aimed to examine the association between CTI-WHtR and incident MACCE stratified by three glycemic groups using data from the nationally representative CHARLS with up to 9 years of follow-up. Specifically, we employed a stepwise covariate adjustment strategy to dissect adiposity-related collinearity, tested for multiplicative interaction by metabolic status, conducted head-to-head comparisons of eight metabolic indices across glycemic groups, and performed comprehensive sensitivity analyses. Methods This study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cohort studies. The completed STROBE checklist is provided as Additional file 1: Table S1 . The analytical plan, including the MACCE definition, glycemic group classification, covariate adjustment strategy, and sensitivity analyses, was finalized before data analysis was initiated. Study population Data were drawn from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative prospective cohort of community-dwelling adults aged 45 years and older.[ 22 ] CHARLS employed a multistage stratified probability-proportional-to-size sampling design covering 150 counties and 450 communities across 28 provinces in mainland China. Baseline data were collected in 2011, with follow-up waves in 2013, 2015, 2018, and 2020. All participants provided written informed consent, and the study was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). From the initial 17,707 baseline participants, exclusions were applied sequentially: (1) self-reported history of heart disease or stroke at baseline (n = 3,154); (2) missing data for fasting blood glucose, triglycerides, C-reactive protein, waist circumference, height, or BMI among the remaining participants (n = 5,871); (3) missing follow-up outcome data (n = 1,689). The final analytic sample comprised 6,993 participants (Fig. 1). A comparison of baseline characteristics between the 6,993 included and 10,714 excluded participants is provided in Additional file 2: Table S2 . While demographic characteristics were broadly comparable (age: 58.8 vs. 59.2 years, SMD = 0.044; sex: 53.9% vs. 51.0% female, SMD = 0.059), notable imbalances were observed for several key variables: CRP levels were substantially lower in the included group (1.55 vs. 4.67 mg/L, SMD = 0.418), as were triglycerides (121.77 vs. 154.62 mg/dL, SMD = 0.301), and HDL-cholesterol was higher (52.15 vs. 48.89 mg/dL, SMD = 0.214). The included sample also had a higher proportion of rural residents (82.9% vs. 70.8%, SMD = 0.287) and a lower prevalence of self-reported hypertension (20.8% vs. 27.0%, SMD = 0.144). These differences indicate that the analytic sample was metabolically healthier than the excluded population, and the implications of this selection pattern are addressed in the Limitations (Strengths and limitations). Exposure variables Eight metabolic indices were calculated from baseline laboratory and anthropometric measurements: CTI = 0.412 × Ln(CRP [mg/L]) + [Ln(TG [mg/dL] × FPG [mg/dL])] / 2 TyG = Ln(TG [mg/dL] × FPG [mg/dL] / 2) CTI-WHtR = CTI × WHtR TyG-WHtR = TyG × WHtR CTI-BMI = CTI × BMI TyG-BMI = TyG × BMI CTI-WC = CTI × WC TyG-WC = TyG × WC where WHtR denotes waist-to-height ratio, WC denotes waist circumference (cm), and BMI denotes body mass index (kg/m 2 ). All indices were standardized (mean = 0, SD = 1) within the full sample for comparability in Cox models. Glycemic group classification Participants were classified into three groups based on baseline fasting plasma glucose (FPG) and diabetes history: NGT (FPG < 100 mg/dL without diabetes history), prediabetes (100 ≤ FPG < 126 mg/dL without diabetes diagnosis), and T2D (FPG ≥ 126 mg/dL, or self-reported physician-diagnosed diabetes, or current use of glucose-lowering medications).[ 11 , 23 ] This single-FPG-based classification approach is consistent with prior CHARLS-based studies and has been shown to produce reproducible results in this cohort. Outcome ascertainment The primary endpoint was MACCE, defined as the first occurrence of any of the following: (1) incident heart disease (self-reported new diagnosis of heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems); (2) incident stroke (self-reported new diagnosis); (3) all-cause death (confirmed via household or community informant reports and cross-referenced with death registries). Secondary endpoints included each component individually. Follow-up time was calculated from baseline (2011) to the date of first event or last available follow-up (2020), whichever came first. The inclusion of all-cause death (rather than cardiovascular-specific death) in the MACCE composite was a pre-specified design choice based on two considerations. First, CHARLS does not provide systematically adjudicated cause-of-death data, and reliance on informant-reported or registry-based cause of death in a community-dwelling population would introduce substantial misclassification bias, particularly for cardiovascular versus non-cardiovascular causes. Prior CHARLS-based studies have consistently adopted all-cause death in composite cardiovascular endpoints for this reason.[ 4 , 11 , 23 ] Second, a substantial proportion of deaths in individuals with metabolic dysfunction are attributable to CVD-related complications that may not be captured as direct “cardiovascular death” in community-based registries (e.g., sudden cardiac death misclassified as unknown cause, or heart-failure-related death coded as respiratory failure). Using all-cause death avoids underestimating the true cardiovascular mortality burden. The analytical implications of this design choice are addressed in the Discussion (the Discussion). Covariates Covariates included age (continuous), sex, education level (illiterate, primary, middle school, high school, college or above), marital status (married/cohabiting vs. other), residence (rural vs. urban), current smoking status, current alcohol consumption, history of hypertension (self-reported or measured systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg), use of lipid-lowering medications, chronic kidney disease (CKD, defined as eGFR < 60 mL/min/1.73m 2 using the CKD-EPI equation), LDL-cholesterol, HDL-cholesterol, and BMI. Variance inflation factors (VIF) were calculated to assess multicollinearity among covariates; all VIF values were below 5, indicating acceptable collinearity levels. The correlation between WHtR and BMI was r = 0.78 in the overall sample (r = 0.76 in NGT, r = 0.79 in prediabetes, r = 0.80 in T2D), confirming substantial but not perfect collinearity between these two adiposity measures. Statistical analysis Baseline characteristics were compared across the three glycemic groups using one-way ANOVA for continuous variables and chi-squared tests for categorical variables. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of CTI-WHtR with each endpoint, stratified by glycemic group. A deliberate three-model stepwise covariate adjustment strategy was employed: Model 1 adjusted for age and sex; Model 2 additionally adjusted for smoking, alcohol use, education, marital status, and residence; Model 3 further adjusted for hypertension, LDL-C, HDL-C, lipid-lowering medication, CKD, and BMI. The deliberate inclusion of BMI in the final step was designed to assess the extent to which the WHtR component of CTI-WHtR contributed independent predictive information beyond overall adiposity—an approach recommended for dissecting shared variance in correlated exposure-covariate pairs.[ 24 ] Due to missing covariate data, the fully adjusted model (Model 3) was based on 6,947 participants (NGT: n = 2,891; prediabetes: n = 3,090; T2D: n = 966), representing 99.3% of the total analytic sample (N = 6,993). CTI-WHtR was analyzed both as a continuous variable (per SD increment) and as quartiles (Q1 as reference). P for linear trend was calculated by entering the median value of each quartile as a continuous variable. Multiplicative interaction between CTI-WHtR (continuous) and glycemic group was tested by including a cross-product term in the pooled Cox model, with P interaction reported. Pairwise interaction P-values for prediabetes versus NGT and T2D versus NGT were also computed. Head-to-head comparisons of eight indices used Harrell's C-statistic from Model 3 for discrimination. Paired comparisons of Harrell’s C-index between CTI-derived and TyG-derived models were performed using bootstrap resampling (B = 1,000, seed = 2024). In each bootstrap iteration, both competing models were refitted on the same resampled dataset to preserve the correlation structure between paired C-indices, and the difference in C-index (ΔC) was computed. The 95% confidence interval was obtained using the percentile method, and a two-sided P-value was calculated as twice the proportion of bootstrap ΔC values crossing zero. This approach naturally accounts for the correlation between paired C-indices estimated on the same subjects and is valid for Harrell’s C-index under censoring, unlike the DeLong test which is designed for binary-outcome AUC comparisons. Subgroup analyses for the prediabetes group examined effect modification by sex, age (< 60 vs. ≥60), BMI (< 24 vs. ≥24 kg/m 2 ), hypertension status, CKD status, and residence. Sensitivity analyses included: (S1) complete case analysis excluding any participants with missing covariates; (S2) exclusion of events within the first 2 years to address reverse causation; (S3) age-and-sex-only adjustment to evaluate baseline association strength; (S4) removal of BMI from the full model to assess the impact of adiposity adjustment; (S5–S7) separate analyses for each MACCE component (heart disease, stroke, all-cause death); (S8) restriction to stable prediabetes participants without progression to diabetes during follow-up; and (S9) Fine-Gray subdistribution hazard model treating all-cause death as a competing event for non-fatal endpoints (heart disease and stroke). Given the multiple comparisons across 288 analyses (8 indices × 3 models × 3 groups × 4 outcomes), we applied a Benjamini–Hochberg false discovery rate (FDR) procedure at q = 0.05 and report both nominal and FDR-adjusted P-values for key findings. The proportional hazards assumption was verified using scaled Schoenfeld residuals (cox.zph function in R). For each stratified Cox model, both a global goodness-of-fit test and variable-specific tests were performed to assess potential time-dependent changes in regression coefficients; no significant violations were detected (all P > 0.05 for both global and individual covariate tests), and visual inspection of scaled Schoenfeld residual plots confirmed no systematic time-dependent trends. All analyses were performed using R version 4.2.2 (survival, survminer, rms, cmprsk packages) and SAS 9.4. Two-sided P < 0.05 was considered statistically significant for the primary analysis. No large language models (LLMs) or artificial intelligence-assisted tools were used in the design, data analysis, or drafting of this manuscript. Results Baseline characteristics The baseline characteristics of the 6,993 participants are presented in Table 1 . The mean age was 58.8 ± 9.6 years, 53.9% were female, and 29.5% were illiterate. Participants with T2D were older, had higher BMI, WHtR, FPG, HbA1c, triglycerides, CRP levels, and higher prevalence of hypertension and lipid-lowering medication use compared with the NGT and prediabetes groups (all P < 0.0001). All eight metabolic indices increased progressively across the NGT, prediabetes, and T2D groups. The MACCE incidence was 18.8%, 21.5%, and 26.0% in the NGT, prediabetes, and T2D groups, respectively. The mean follow-up was 8.0 ± 2.0 years, with the T2D group having a slightly shorter follow-up duration (7.68 ± 2.24 years) than the NGT (8.08 ± 1.95 years) and prediabetes (8.06 ± 1.96 years) groups. Table 1 Baseline Characteristics of Study Participants by Glycemic Status (N = 6,993) Variable NGT (n = 2,916) Prediabetes (n = 3,106) T2D (n = 971) Total (n = 6,993) P-value Age (years) 57.92 ± 9.81 59.17 ± 9.40 60.13 ± 9.37 58.78 ± 9.60 < 0.0001 Female, % 53.7 53.7 54.9 53.9 0.80 Age ≥ 60, % 39.4 44.9 49.1 43.2 < 0.0001 Education, % Illiterate 29.2 29.7 29.6 29.5 0.12 Primary school 41.5 39.8 41.5 40.8 Middle school 18.9 21.1 19.2 19.9 High school 7.8 6.4 6.2 6.9 College or above 2.6 3.0 3.5 2.9 Married/cohabiting, % 89.3 87.3 88.0 88.2 0.07 BMI (kg/m 2 ) 22.79 ± 3.46 23.55 ± 3.69 24.26 ± 3.64 23.33 ± 3.63 < 0.0001 WHtR 0.53 ± 0.06 0.54 ± 0.07 0.56 ± 0.06 0.54 ± 0.06 < 0.0001 FPG (mg/dL) 91.51 ± 7.02 108.10 ± 7.01 156.77 ± 61.00 107.94 ± 31.66 < 0.0001 HbA1c (%) 5.01 ± 0.33 5.20 ± 0.41 6.20 ± 1.56 5.26 ± 0.78 < 0.0001 TG (mg/dL) 105.28 ± 53.80 127.59 ± 74.84 152.63 ± 92.57 121.77 ± 71.72 < 0.0001 CRP (mg/L) 1.40 ± 1.56 1.59 ± 1.64 1.85 ± 1.76 1.55 ± 1.63 < 0.0001 HDL-C (mg/dL) 53.32 ± 14.35 52.20 ± 15.40 48.45 ± 15.53 52.15 ± 15.07 < 0.0001 LDL-C (mg/dL) 114.70 ± 31.07 121.19 ± 35.68 119.11 ± 36.68 118.19 ± 34.11 < 0.0001 eGFR (mL/min/1.73m 2 ) 93.57 ± 14.25 91.94 ± 14.26 90.49 ± 15.65 92.42 ± 14.49 < 0.0001 CTI-WHtR 2.38 ± 0.42 2.56 ± 0.46 2.82 ± 0.51 2.52 ± 0.47 < 0.0001 TyG index 8.37 ± 0.47 8.69 ± 0.53 9.17 ± 0.70 8.62 ± 0.60 < 0.0001 Hypertension, % 16.0 21.5 31.9 20.8 < 0.0001 Current smoking, % 31.9 29.3 28.7 30.3 0.08 Lipid-lowering medication, % 1.7 3.0 8.1 3.2 < 0.0001 CKD (eGFR < 60), % 2.2 3.0 4.6 2.9 0.002 MACCE, n (%) 547 (18.8) 668 (21.5) 252 (26.0) 1,467 (21.0) < 0.0001 Heart disease, n (%) 426 (14.6) 488 (15.7) 177 (18.2) 1,091 (15.6) 0.03 Stroke, n (%) 170 (5.8) 255 (8.2) 104 (10.7) 529 (7.6) < 0.0001 All-cause death, n (%) 305 (10.5) 333 (10.7) 148 (15.2) 786 (11.2) 0.0004 Follow-up time (years) 8.08 ± 1.95 8.06 ± 1.96 7.68 ± 2.24 8.02 ± 2.00 < 0.0001 Data are presented as mean ± SD or n (%). P-values from one-way ANOVA for continuous variables and chi-squared tests for categorical variables. NGT, normal glucose tolerance; T2D, type 2 diabetes; FPG, fasting plasma glucose; TG, triglycerides; CRP, C-reactive protein; WHtR, waist-to-height ratio; CTI, C-reactive protein–triglyceride–glucose index; TyG, triglyceride-glucose index; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; MACCE, major adverse cardiovascular and cerebrovascular events. [Figure 1 — Insert image file here (TIFF, ≥ 300 dpi)] Figure 1 Flow diagram of participant selection from the CHARLS cohort. A total of 9,025 participants were excluded for baseline CVD or missing key laboratory/anthropometric variables, and a further 1,689 for missing follow-up data, yielding a final analytic sample of 6,993 participants classified into three glycemic groups. NGT, normal glucose tolerance; T2D, type 2 diabetes; FPG, fasting plasma glucose; CVD, cardiovascular disease; MACCE, major adverse cardiovascular and cerebrovascular events. Primary analysis: CTI-WHtR and MACCE by glycemic group Table 2 presents the associations between CTI-WHtR and MACCE across three glycemic groups and three adjustment models. In the NGT group, CTI-WHtR was consistently and significantly associated with MACCE across all models. After full adjustment (Model 3, n = 2,891), each SD increment in CTI-WHtR was associated with a 26% increased risk of MACCE (HR 1.26, 95% CI 1.11–1.43, P = 0.0003). The quartile analysis revealed a significant dose–response relationship (Q4 vs. Q1: HR 1.34, 95% CI 1.00–1.81, P = 0.051; P-trend = 0.018). Notably, the HR decreased only modestly from Model 1 (HR 1.37) to Model 3 (HR 1.26) upon BMI adjustment, indicating that CTI-WHtR retained substantial independent predictive information beyond adiposity in the NGT group. In contrast, the association was substantially attenuated in the prediabetes group after full adjustment. The per-SD HR decreased from 1.19 (95% CI 1.10–1.29, P < 0.0001) in Model 1 to 1.04 (95% CI 0.94–1.16, P = 0.43) in Model 3, and the Q4-versus-Q1 HR decreased from 1.64 (P < 0.0001) to 1.21 (P = 0.20), indicating that the predictive signal was largely explained by covariates, particularly BMI. In the T2D group, a similar pattern of attenuation was observed: the per-SD HR decreased from 1.31 (Model 1, P < 0.0001) to 1.12 (Model 3, P = 0.17), and the Q4-versus-Q1 HR decreased from 2.44 (P < 0.0001) to 1.60 (P = 0.064; P-trend = 0.082). Notably, the T2D Model 3 Q4 HR of 1.60 (95% CI 0.97–2.62) approached significance, suggesting a possible residual dose–response relationship that was underpowered in this smaller subgroup (n = 966, 252 events). Table 2 Association Between CTI-WHtR and MACCE Stratified by Glycemic Group Group Model HR per SD (95% CI) P Q4 vs Q1 HR (95% CI) P-trend NGT (n = 2,916; 547 events) Model 1 1.37 (1.24–1.50) < 0.0001 1.79 (1.40–2.28) < 0.0001 Model 2 1.38 (1.26–1.52) < 0.0001 1.82 (1.42–2.33) < 0.0001 Model 3 † 1.26 (1.11–1.43) 0.0003 1.34 (1.00–1.81) 0.018 Prediabetes (n = 3,106; 668 events) Model 1 1.19 (1.10–1.29) < 0.0001 1.64 (1.30–2.06) < 0.0001 Model 2 1.19 (1.10–1.28) < 0.0001 1.63 (1.29–2.06) < 0.0001 Model 3 † 1.04 (0.94–1.16) 0.43 1.21 (0.91–1.61) 0.32 T2D (n = 971; 252 events) Model 1 1.31 (1.17–1.47) < 0.0001 2.44 (1.66–3.58) < 0.0001 Model 2 1.32 (1.17–1.48) < 0.0001 2.46 (1.67–3.64) < 0.0001 Model 3 † 1.12 (0.95–1.32) 0.17 1.60 (0.97–2.62) 0.082 Model 1: adjusted for age and sex. Model 2: Model 1 + smoking, alcohol use, education, marital status, residence. Model 3: Model 2 + hypertension, LDL-C, HDL-C, lipid-lowering medication, CKD, BMI. † Model 3 sample sizes: NGT n = 2,891; Prediabetes n = 3,090; T2D n = 966 (reduced due to missing covariates). HR, hazard ratio; CI, confidence interval; SD, standard deviation; Q, quartile. The association between CTI-WHtR and individual MACCE components is shown in Fig. 2. The strongest endpoint-specific association was observed for stroke in the NGT group (Model 3: HR 1.71, 95% CI 1.37–2.14, P < 0.0001), which remained robust after FDR correction. In the prediabetes group, CTI-WHtR showed significant associations with stroke (HR 1.22, 95% CI 1.03–1.45, P = 0.025) and all-cause death (HR 1.33, 95% CI 1.14–1.56, P = 0.0004) but not with MACCE or heart disease. In the T2D group, only the association with all-cause death reached significance (HR 1.26, 95% CI 1.02–1.56, P = 0.032). These findings reveal a consistent pattern of progressive attenuation from NGT to prediabetes to T2D across all endpoints—a phenomenon we term the “predictive paradox,” defined as the observation that CTI-WHtR showed the strongest independent predictive value in the metabolically healthiest group (NGT), despite stronger crude (unadjusted) associations in groups with greater metabolic impairment. [Figure 2 — Insert image file here (TIFF, ≥ 300 dpi)] Figure 2 CTI-WHtR associations with cardiovascular endpoints by glycemic group (Model 3). Forest plot showing hazard ratios per SD increment in CTI-WHtR for four endpoints (MACCE, heart disease, stroke, all-cause death) across three glycemic groups (NGT, prediabetes, T2D). The x-axis is on a logarithmic scale. Circles represent point estimates of hazard ratios; horizontal lines indicate 95% confidence intervals. The dashed vertical line represents the null value (HR = 1.0). Bold P-values indicate statistical significance (P < 0.05). Interaction analysis Table 3 presents the results of multiplicative interaction tests between glycemic group and CTI-WHtR for each endpoint. Pairwise interaction analysis revealed a statistically significant difference in the CTI-WHtR–MACCE association between the prediabetes and NGT groups (P = 0.027 in Model 3), and between the T2D and NGT groups for the stroke endpoint (P = 0.029), confirming that the markedly stronger stroke association in the NGT group (HR 1.71) compared with the T2D group (HR 1.08) was unlikely due to chance. The overall three-group interaction tests yielded P interaction values suggestive of effect modification (P = 0.071 for MACCE; P = 0.056 for stroke in Model 3)—a pattern consistent with effect modification but reflecting the reduced statistical power inherent in the smaller T2D subgroup (n = 971). For heart disease, a similar interaction pattern was observed (overall P = 0.077; prediabetes vs. NGT P = 0.034). No significant interaction was observed for all-cause death, consistent with the finding that CTI-WHtR predicted mortality across all three glycemic groups. Table 3 Interaction Tests Between Glycemic Group and CTI-WHtR for Cardiovascular Endpoints Endpoint Model P interaction (overall) P (Prediabetes vs NGT) P (T2D vs NGT) MACCE Model 1 0.145 0.061 0.698 Model 3 0.071 0.027 0.611 Heart disease Model 1 0.135 0.065 0.871 Model 3 0.077 0.034 0.787 Stroke Model 1 0.086 0.069 0.039 Model 3 0.056 0.042 0.029 All-cause death Model 1 0.908 0.851 0.801 Model 3 0.990 0.888 0.924 P interaction from the multiplicative interaction term (CTI-WHtR × glycemic group) in pooled Cox models. Bold values indicate P < 0.05. Model 1: age + sex. Model 3: fully adjusted including BMI. Note: pairwise interaction tests provide complementary evidence to the overall three-group test, as the latter is susceptible to power limitations when one group (T2D, n = 971) is substantially smaller than the others. Head-to-head comparison of eight metabolic indices Table 4 presents the fully adjusted (Model 3) HRs and C-index values for all eight metabolic indices predicting MACCE in each glycemic group. In the NGT group, CTI-based indices consistently achieved numerically higher C-index values and stronger statistical significance than TyG-based indices, although the absolute C-index differences were small (0.005–0.008). The highest C-index was achieved by CTI-WC (0.635), followed by CTI-BMI (0.633), CTI (0.632), and CTI-WHtR (0.632). All four CTI-based indices were statistically significant (all P < 0.001). In contrast, among TyG-based indices, only TyG-WC reached significance (HR 1.20, P = 0.007), while TyG-BMI (HR 1.11, P = 0.48), TyG (HR 1.04, P = 0.52), and TyG-WHtR (HR 1.12, P = 0.11) were non-significant. In the prediabetes group, no index achieved statistical significance after full adjustment, with C-indices clustering narrowly between 0.627 and 0.628. In the T2D group, a numerical reversal was observed: TyG-WC (HR 1.25, C = 0.654), TyG-BMI (HR 1.36, C = 0.651), and TyG (HR 1.14, C = 0.652) emerged as significant predictors, while CTI-WHtR (HR 1.12, P = 0.17) and CTI-BMI (HR 1.19, P = 0.14) were non-significant. Paired bootstrap comparisons of C-indices between matched CTI and TyG index pairs formally quantified these differences (Additional file 3: Table S3 ). In the NGT group, CTI-based indices consistently showed higher C-indices than their TyG counterparts, with the largest difference observed for CTI-BMI versus TyG-BMI (ΔC = + 0.006, 95% CI − 0.001 to + 0.016, P = 0.116) and CTI-WC versus TyG-WC (ΔC = + 0.006, 95% CI 0.000 to + 0.014, P = 0.050). For the primary exposure, CTI-WHtR achieved a numerically but non-significantly higher C-index than TyG-WHtR (ΔC = + 0.005, 95% CI − 0.001 to + 0.013, P = 0.076). In the T2D group, the direction reversed: TyG-WC showed the largest advantage over CTI-WC (ΔC = + 0.003, 95% CI − 0.004 to + 0.011, P = 0.480), though none reached significance. In the prediabetes group, all differences were negligible (ΔC ≤ 0.001, all P > 0.67). Thus, while the directional pattern of group-switching was consistent across all four index pairs, the absolute C-index differences (0.001–0.006) were small and did not reach statistical significance, indicating that the group-switching phenomenon represents a descriptive trend requiring confirmation in larger studies. This “group-switching” pattern is also visualized in Fig. 3. Table 4 Head-to-Head Comparison of Eight Metabolic Indices for MACCE Prediction (Model 3) Index NGT (n = 2,891; 547 events) Prediabetes (n = 3,090; 664 events) T2D (n = 966; 252 events) HR P C-index HR P C-index HR P C-index CTI-WC 1.33 < 0.0001 0.635 1.07 0.24 0.628 1.17 0.047 0.652 CTI-BMI 1.45 0.0002 0.633 1.05 0.55 0.628 1.19 0.14 0.650 CTI 1.20 0.0004 0.632 1.04 0.42 0.628 1.11 0.088 0.650 CTI-WHtR 1.26 0.0003 0.632 1.04 0.43 0.628 1.12 0.17 0.649 TyG-WC 1.20 0.007 0.630 1.05 0.43 0.627 1.25 0.009 0.654 TyG-WHtR 1.12 0.11 0.627 1.02 0.74 0.627 1.18 0.058 0.651 TyG 1.04 0.52 0.627 1.01 0.77 0.628 1.14 0.023 0.652 TyG-BMI 1.11 0.48 0.627 0.99 0.90 0.628 1.36 0.033 0.651 All HRs are per SD increment. Model 3: fully adjusted including BMI. C-index: Harrell’s concordance statistic. Bold P-values indicate statistical significance (P < 0.05). Indices are ordered by C-index within the NGT group. CTI-based indices are shown in bold; TyG-based indices in regular font. The dashed line separates CTI-based from TyG-based indices. C-index values (Harrell’s C via survival::concordance) are computed from the complete-case Model 3 sample (NGT n = 2,854; Prediabetes n = 3,041; T2D n = 953), consistent with the bootstrap sample used in Additional file 3: Table S3 . HRs are from the primary Model 3 sample (per Table 2 footnote). Formal paired bootstrap comparisons of C-indices between matched CTI and TyG index pairs (B = 1,000) are presented in Additional file 3: Table S3 ; while the directional pattern of CTI superiority in NGT and TyG superiority in T2D was consistent across all index pairs, the absolute ΔC-index differences (0.001–0.006) were small and did not reach statistical significance at P < 0.05. [Figure 3 — Insert image file here (TIFF, ≥ 300 dpi)] Figure 3C -index comparison of eight metabolic indices across glycemic groups. Dot plot comparing Harrell’s C-index for MACCE prediction among eight metabolic indices in the NGT (blue circles) and T2D (red diamonds) groups under Model 3. The dashed horizontal line separates CTI-based indices (above) from TyG-based indices (below). In the NGT group, CTI-based indices consistently achieved higher C-indices than TyG-based indices. In the T2D group, the pattern reversed: TyG-based indices achieved higher C-indices. Asterisks indicate indices with statistically significant HRs per SD (P < 0.05). The prediabetes group is not shown because all indices were non-significant after full adjustment (C-index range: 0.627–0.628). Subgroup analysis To further explore the non-significant association in the prediabetes group, we performed subgroup analyses for MACCE (Table 5 ). No subgroup demonstrated a statistically significant association between CTI-WHtR and MACCE in the fully adjusted model, although the elderly subgroup (age ≥ 60 years) approached significance (HR 1.16, 95% CI 1.00–1.34, P = 0.053) and the overweight subgroup (BMI ≥ 24) showed a trend (HR 1.09, 95% CI 0.96–1.25, P = 0.19). The CKD subgroup (n = 92, 14 events) was too small for stable model estimation and is reported without an HR estimate. No significant interaction was detected across any subgroup variable (all P interaction >0.10), indicating that the null association in the prediabetes group was consistent across sex, age, BMI, hypertension status, CKD status, and residence rather than being masked by subgroup heterogeneity. Table 5 Subgroup Analysis: CTI-WHtR and MACCE in the Prediabetes Group (Model 3) Subgroup N Events HR per SD (95% CI) P Overall 3,090 664 1.04 (0.94–1.16) 0.43 Male 1,429 278 1.09 (0.92–1.29) 0.33 Female 1,661 386 1.01 (0.88–1.16) 0.86 Age < 60 years 1,699 314 0.97 (0.82–1.14) 0.69 Age ≥ 60 years 1,391 350 1.16 (1.00–1.34) 0.053 BMI < 24 kg/m 2 1,792 345 1.06 (0.92–1.23) 0.39 BMI ≥ 24 kg/m 2 1,298 319 1.09 (0.96–1.25) 0.19 No hypertension 2,421 455 1.07 (0.94–1.22) 0.33 Hypertension 669 209 1.01 (0.84–1.22) 0.88 No CKD 2,998 650 1.04 (0.93–1.16) 0.49 CKD ‡ 92 14 — — Rural residence 2,522 536 1.01 (0.89–1.14) 0.91 Urban residence 568 128 1.14 (0.91–1.43) 0.26 Model 3: fully adjusted. HR per SD increment in CTI-WHtR. All subgroup interaction P-values > 0.10. ‡ CKD subgroup (n = 92, 14 events): HR not reported due to insufficient sample size for stable estimation in the fully adjusted model. Sensitivity analyses The results of sensitivity analyses are shown in Fig. 4. In the NGT group, the association between CTI-WHtR and MACCE remained robust across all sensitivity analyses: complete case analysis (HR 1.27, 95% CI 1.12–1.45, P = 0.0002), exclusion of events within the first 2 years (HR 1.17, 95% CI 1.02–1.35, P = 0.027), and the age-and-sex-only model (HR 1.37, 95% CI 1.24–1.50, P < 0.0001). Removing BMI from the full model yielded a higher HR (1.33 vs. 1.26, 95% CI 1.20–1.47, P < 0.0001), confirming that BMI adjustment partially but not fully attenuated the association. The stroke endpoint showed the strongest association (HR 1.71, 95% CI 1.37–2.14, P < 0.0001), while heart disease (HR 1.20, P = 0.013) and all-cause death (HR 1.28, P = 0.008) were also significant. Fine-Gray competing risk analysis (S9), treating all-cause death as a competing event, yielded consistent subdistribution HRs for heart disease (SHR 1.18, 95% CI 1.03–1.35, P = 0.016) and stroke (SHR 1.49, 95% CI 1.21–1.83, P = 0.0001), confirming that the NGT findings were not driven by competing mortality risk. In the prediabetes group, CTI-WHtR showed significant associations only in the simpler models (age + sex only: HR 1.19, P < 0.0001; without BMI: HR 1.11, P = 0.009) and for specific endpoints (stroke: HR 1.22, P = 0.025; all-cause death: HR 1.33, P = 0.0004). Restricting to stable prediabetes participants who did not progress to diabetes during follow-up yielded a null result (HR 1.03, 95% CI 0.92–1.15, P = 0.61). In the T2D group, only all-cause death reached significance in Model 3 (HR 1.26, 95% CI 1.02–1.56, P = 0.032), but removal of BMI from the model restored significance for MACCE (HR 1.23, 95% CI 1.09–1.39, P = 0.0008), further supporting the collinearity hypothesis. Fine-Gray competing risk analysis (S9) confirmed these null findings: in the prediabetes group, subdistribution HRs for heart disease (SHR 1.00, P = 0.95) and stroke (SHR 1.15, P = 0.16) were non-significant; in the T2D group, SHRs for heart disease (SHR 0.98, P = 0.86) and stroke (SHR 1.01, P = 0.95) were similarly null. [Figure 4 — Insert image file here (TIFF, ≥ 300 dpi)] Figure 4 Sensitivity analyses for CTI-WHtR associations across glycemic groups. Analyses for the association between CTI-WHtR (per SD) and cardiovascular endpoints across three glycemic groups. The x-axis is on a logarithmic scale. Circles represent hazard ratios; horizontal lines represent 95% confidence intervals. The dashed line indicates the null value (HR = 1.0). Bold P-values indicate statistical significance (P < 0.05). Primary analysis uses Model 3 (fully adjusted including BMI) unless otherwise specified. S9 reports subdistribution hazard ratios (SHR) from the Fine-Gray model treating all-cause death as a competing event for non-fatal endpoints (heart disease and stroke); all other analyses report cause-specific hazard ratios from Cox regression. S8 (restriction to stable prediabetes participants without progression to diabetes) is shown only in the Prediabetes panel, as it applies exclusively to this glycemic group and is not applicable to the NGT or T2D groups. Discussion Principal findings To our knowledge, this is the first study to systematically examine how metabolic status modifies the predictive value of CTI-WHtR for MACCE in a large, nationally representative cohort with nearly a decade of prospective follow-up. Four key findings emerged. First, CTI-WHtR independently predicted MACCE in the NGT group after comprehensive covariate adjustment including BMI (HR 1.26, 95% CI 1.11–1.43, P = 0.0003), but this association was attenuated to non-significance in the prediabetes and T2D groups—a phenomenon we term the “predictive paradox.” Second, formal multiplicative interaction testing provided evidence of effect modification, with overall P-interaction values suggestive of modification (P = 0.071 for MACCE; P = 0.056 for stroke) corroborated by statistically significant pairwise comparisons (prediabetes vs. NGT P = 0.027 for MACCE; T2D vs. NGT P = 0.029 for stroke). Third, restricted cubic spline dose–response analyses revealed a qualitative transformation: the CTI-WHtR–outcome relationship was consistently linear in the NGT group (all P-non-linearity > 0.25) but shifted to a significantly non-linear, inverted-U-shaped pattern in the T2D group (P-non-linearity = 0.017 for MACCE, 0.017 for stroke, 0.023 for all-cause death)—indicating not merely quantitative weakening but a fundamental change in dose–response architecture. Fourth, head-to-head comparison of eight metabolic indices revealed a “group-switching” pattern, with CTI-based indices outperforming TyG-based indices in the NGT group and vice versa in the T2D group. Paired bootstrap comparisons confirmed the directional consistency across all four matched index pairs, although absolute ΔC-index differences (0.001–0.006) did not reach statistical significance. These findings have important implications for metabolic-status-specific cardiovascular risk stratification. Comparison with existing literature Table 6 compares the design features and key findings of the present study with prior CHARLS-based studies examining metabolic indices and cardiovascular outcomes. Our study is the first to examine CTI-WHtR specifically, use MACCE as a composite endpoint, employ three-group glycemic stratification, and conduct head-to-head comparison of eight indices with formal interaction testing. Table 6 Comparison of the Present Study with Prior CHARLS-Based Studies on Metabolic Indices and CVD Feature Present Study Huo 2025[ 11 ] Yue 2025[ 18 ] Ye 2022[ 23 ] Yang 2025[ 30 ] Primary exposure CTI-WHtR CTI CTI-WHtR TyG Cumulative CTI Primary outcome MACCE (composite) Stroke Stroke CVD composite Stroke Glycemic stratification 3-group (NGT/Pre/T2D) 3-group None 2-group None Interaction testing Yes (formal) No No No No Head-to-head (8 indices) Yes No Partial (4) No No Sensitivity analyses 9 types 3 types 4 types 2 types 5 types Multiple comparison correction FDR No No No No Sample size 6,993 ~ 7,500 ~ 8,200 ~ 5,600 ~ 7,000 Key finding Predictive paradox + group-switching CTI predicts stroke in NGT CTI-WHtR best for stroke TyG–CVD modified by DM Cumulative CTI ↑ stroke MACCE, major adverse cardiovascular and cerebrovascular events; CTI, C-reactive protein–triglyceride–glucose index; WHtR, waist-to-height ratio; TyG, triglyceride-glucose index; NGT, normal glucose tolerance; Pre, prediabetes; T2D, type 2 diabetes; FDR, false discovery rate; DM, diabetes mellitus. Our finding that CTI-WHtR predicts MACCE in normoglycemic individuals is broadly consistent with prior CHARLS-based studies. Huo et al. reported that CTI was significantly associated with stroke risk in those with normal glucose regulation (per-unit HR approximately 1.44) and that this association was attenuated in prediabetes.[ 11 ] Zhang et al. and Xu et al. independently confirmed the association between CTI and incident CVD using CHARLS data, further supporting the predictive utility of CTI-based indices in community-dwelling Chinese adults.[ 14 , 17 ] We extend this observation to the composite MACCE endpoint and the CTI-WHtR composite specifically, and demonstrate for the first time that the attenuation pattern holds across all four MACCE components. The direction of our NGT-group stroke finding (HR 1.71 per SD) is also concordant with Yue et al., who showed that CTI-WHtR was the optimal stroke predictor among CTI derivatives.[ 18 ] The observation that metabolic status modifies TyG–CVD associations is supported by the recent meta-analysis by Zhang et al. across 50 cohorts involving 7.2 million participants, which found stronger TyG–ischemic heart disease associations in non-diabetic individuals (pooled interaction P < 0.05).[ 20 ] Concordantly, Li et al. demonstrated that the TyG index was an independent predictor of MACCE specifically in non-diabetic individuals.[ 25 ] Our study extends this finding from TyG to the more comprehensive CTI-WHtR composite at finer granularity (three glycemic groups rather than two). For all-cause mortality, our observation that CTI-WHtR retained predictive value across all three glycemic groups is concordant with the NHANES-based findings by Liu et al.[ 26 ] and with the multi-dataset analysis by Ni et al., who reported significant associations between CTI and both cardiovascular and all-cause mortality in elderly populations.[ 16 ] The predictive paradox The central and most novel finding of this study is the progressive attenuation of CTI-WHtR’s predictive value from NGT to prediabetes to T2D for MACCE, heart disease, and stroke, despite stronger crude associations in the T2D group (unadjusted Q4 HR 2.44). We propose three non-mutually-exclusive mechanistic explanations for this paradox. First, the collinearity hypothesis: CTI-WHtR is the product of CTI and WHtR, where WHtR is strongly correlated with BMI (r = 0.78 overall; r = 0.80 in T2D). When BMI was included in Model 3, the per-SD HR in the prediabetes group decreased from 1.19 to 1.04 (Δ = −0.15, a 79% reduction in excess risk), whereas in the NGT group it decreased from 1.37 to 1.26 (Δ = −0.11, a 30% reduction). This indicates that in the prediabetes and T2D groups, the WHtR component is largely redundant with BMI, whereas in the NGT group CTI-WHtR captures independent residual risk beyond overall adiposity. The sensitivity analysis removing BMI directly supported this: without BMI adjustment, the prediabetes group showed HR 1.11 (P = 0.009) and the T2D group HR 1.23 (P = 0.0008). Second, the pharmacological confounding hypothesis: participants with prediabetes and T2D were more likely to use lipid-lowering medications (3.0% and 8.1%, respectively, vs. 1.7% in NGT), antihypertensives, and glucose-lowering agents. These medications can substantially alter triglycerides, fasting glucose, and inflammatory markers—the very components of CTI-WHtR—creating a disconnect between the measured index value and the underlying biological risk.[ 27 ] Third, the competing risk dilution hypothesis: as metabolic disease progresses from NGT to T2D, individuals accumulate additional strong risk factors—nephropathy, neuropathy, and advanced atherosclerosis—that independently drive MACCE risk. The marginal predictive contribution of CTI-WHtR is diluted by these competing risk factors that are only partially captured by our covariates. This interpretation is supported by the C-index convergence in the prediabetes group (all eight indices yielded C-indices of 0.627–0.628), suggesting limited discriminatory capacity when the baseline risk profile is homogeneously elevated. Notably, our Fine-Gray competing risk analysis (S9) demonstrated that accounting for death as a competing event did not materially alter the subdistribution hazard ratios for heart disease or stroke in any glycemic group, suggesting that the attenuation in the prediabetes and T2D groups reflects genuine effect dilution rather than an artifact of differential mortality. Beyond these mechanistic explanations, RCS dose–response analyses (Additional file 5: Table S5 ) revealed a finding of potentially greater conceptual significance: the predictive paradox reflects not simply an attenuation of effect size but a fundamental transformation in the dose–response architecture itself. In the NGT group, the association between CTI-WHtR and all four endpoints was unambiguously linear and monotonic (P-non-linearity = 0.260–0.990), with hazard ratios increasing proportionally across the full exposure range. This linearity validates the per-SD HR as a faithful summary of the true dose–response relationship in normoglycemic individuals. In stark contrast, the T2D group exhibited statistically significant non-linearity for three of four endpoints: MACCE (P-non-linearity = 0.017), stroke (P-non-linearity = 0.017), and all-cause death (P-non-linearity = 0.023). The RCS curves consistently demonstrated an inverted-U-shaped (dome-shaped) dose–response pattern, with hazard ratios rising steeply in the lower-to-middle CTI-WHtR range, peaking at approximately 3.0, and then declining at higher values. This finding carries two immediate implications. First, it reframes the non-significant per-SD hazard ratios in the T2D group: they likely reflect the mathematical artifact of fitting a linear model to a genuinely non-linear relationship, where the upward and downward limbs of the inverted-U partially cancel each other, yielding a falsely attenuated linear slope. Second, it suggests a “risk saturation threshold” in diabetic populations, beyond which further metabolic deterioration paradoxically fails to confer additional cardiovascular risk. More broadly, this demonstrates that metabolic status modifies not only the magnitude but also the functional form of biomarker–outcome associations—a dimension of effect modification largely unrecognized in the metabolic index literature. Extended discussion of these implications is provided in Additional file 6. It is important to note that the MACCE definition includes all-cause death, which demonstrated significant associations with CTI-WHtR across all three glycemic groups (NGT HR 1.28, prediabetes HR 1.33, T2D HR 1.26), with no evidence of effect modification (P-interaction = 0.990). This indicates that the “predictive paradox” was primarily driven by the heart disease and stroke components rather than by all-cause death. Had MACCE been defined without all-cause death, the differential attenuation across glycemic groups would likely have been more pronounced, given that all-cause death was the only component showing consistent associations without interaction. This strengthens our conclusion regarding metabolic status modification of CTI-WHtR’s predictive value. The group-switching phenomenon A particularly intriguing finding was the reversal in performance between CTI-based and TyG-based indices across glycemic groups. In the NGT group, all four CTI-based indices achieved significant HRs and higher C-indices (0.632–0.635) than TyG-based indices (0.627–0.630), with most TyG indices being non-significant. In the T2D group, TyG-WC (HR 1.25, C = 0.654), TyG-BMI (HR 1.36, C = 0.651), and TyG (HR 1.14, C = 0.652) all achieved significance, while CTI-WHtR and CTI-BMI did not. Paired bootstrap comparisons confirmed directional consistency across all four matched index pairs, although absolute ΔC-index differences were small (0.001–0.006) and did not reach statistical significance (Additional file 3: Table S3 ). The CTI-WC versus TyG-WC comparison in the NGT group came closest to significance (ΔC = + 0.006, P = 0.050). The convergence of consistent directional patterns across all pairs, despite individually non-significant tests, provides suggestive evidence warranting confirmation in larger cohorts. We interpret this pattern through the lens of the evolving pathobiology of atherosclerosis across the metabolic spectrum.[ 28 ] In the NGT stage, overt glucolipid derangement is minimal (mean TG 105 mg/dL, FPG 92 mg/dL), and the predominant signal distinguishing individuals at elevated cardiovascular risk is subclinical inflammation. CRP, as a marker of this low-grade inflammatory state, adds critical prognostic information that the pure glucose-lipid TyG signal cannot capture. In the T2D stage, glucolipid perturbations are severe (mean TG 153 mg/dL, FPG 157 mg/dL) and dominate the pathophysiological landscape. In this setting, the direct glucose-lipid signal captured by TyG becomes more informative than the inflammation-weighted CTI signal, because the magnitude of glucolipid dysregulation now overwhelms any additional information provided by CRP. This interpretation aligns with the established model wherein early-stage atherosclerosis is driven by endothelial inflammation and immune activation, while advanced disease features lipid core expansion and plaque destabilization driven by metabolic burden.[ 28 ] Notably, Hong et al. also reported differential predictive performance between TyG and modified TyG indices across cardiovascular-kidney-metabolic syndrome stages, supporting the concept that the relative utility of metabolic indices varies with the severity of metabolic derangement.[ 29 ] Clinical implications Our findings have four practical implications. First, CTI-WHtR may serve as an effective and accessible screening tool for cardiovascular risk specifically in the normoglycemic population—a group that constituted 41.7% of our cohort (n = 2,916) and experienced 547 MACCE events, yet is typically not targeted by conventional diabetes-oriented screening programs. CTI-WHtR requires only routine laboratory tests (CRP, triglycerides, fasting glucose) and basic anthropometry (waist circumference, height), making it feasible for community-based screening. Second, the group-switching phenomenon suggests that a one-size-fits-all approach to metabolic index selection is suboptimal. Clinicians and public health programs may benefit from adopting metabolic-status-specific screening strategies: CTI-based indices (particularly CTI-WC, C = 0.635) for normoglycemic populations and TyG-based indices (particularly TyG-WC, C = 0.654) for those with established diabetes. Third, the discovery of an inverted-U-shaped dose–response in the T2D group carries a direct methodological caution: when evaluating metabolic indices in diabetic populations, relying solely on per-SD hazard ratios from linear Cox models may lead to the erroneous conclusion that no association exists; non-linear modeling (e.g., RCS) should be incorporated as a standard analytical step. The risk saturation threshold identified at approximately CTI-WHtR 3.0 in the T2D group could, if replicated, serve as a clinically actionable cut-point above which additional metabolic deterioration no longer confers incremental cardiovascular risk. Fourth, the finding that no index performed well in the prediabetes group after full adjustment suggests that prediabetes may represent a transitional “predictive blind spot” where traditional metabolic indices have limited incremental value beyond conventional risk factors, and where novel biomarkers or multi-dimensional risk scores may be needed. Strengths and limitations This study has several notable strengths: (a) a large sample size (N = 6,993) from a nationally representative cohort ensuring generalizability to the middle-aged and older Chinese population; (b) nearly 9 years of prospective follow-up with 1,467 MACCE events providing adequate statistical power; (c) systematic comparison of all eight CTI and TyG index variants across three glycemic groups using a unified analytical framework; (d) a deliberately designed stepwise adjustment strategy that allowed dissection of adiposity-related collinearity, supported by quantitative VIF and correlation data; (e) formal multiplicative interaction testing with pairwise comparisons; (f) paired bootstrap C-index comparisons between matched index pairs; (g) nine sensitivity analyses including competing risk approaches, stable-subgroup restriction, and early-event exclusion; and (h) multiple comparison correction using the Benjamini–Hochberg FDR procedure. Several limitations should be acknowledged. First, a substantial proportion of baseline participants (5,871 of 17,707, 33.2%) were excluded due to missing laboratory or anthropometric data, and an additional 1,689 for missing follow-up data, which may introduce selection bias if data were not missing completely at random. The included sample was metabolically healthier than the excluded population (CRP: 1.55 vs. 4.67 mg/L, SMD = 0.418; triglycerides: 121.77 vs. 154.62 mg/dL, SMD = 0.301; HDL-C: 52.15 vs. 48.89 mg/dL, SMD = 0.214), which could limit generalizability to populations with higher inflammatory or metabolic burden. However, demographic variables showed negligible imbalances (age: SMD = 0.044; sex: SMD = 0.059), and the missingness is plausibly missing at random conditional on observed covariates, as phlebotomy availability in CHARLS was primarily driven by logistics and participant willingness rather than health status. Our complete case sensitivity analysis yielded virtually identical results (NGT: HR 1.27 vs. 1.26). Multiple imputation was not performed due to the complexity of the stratified analytical framework. Extended discussion of the missing data mechanism is provided in Additional file 6. Second, glycemic classification was based on a single fasting glucose measurement without oral glucose tolerance testing or repeated HbA1c measurements, which may introduce misclassification bias. However, single-FPG-based classification is standard practice in CHARLS-based studies, and any resulting non-differential misclassification would likely bias associations toward the null, making our NGT findings more conservative.[ 11 , 23 ] Third, MACCE was ascertained through biennial self-report and death registries rather than centrally adjudicated clinical events, which may introduce outcome misclassification. Importantly, the accuracy of self-reported cardiovascular events may vary across education levels and cognitive function, both of which are correlated with glycemic status, raising the possibility of differential outcome misclassification across glycemic strata that could bias interaction estimates in either direction. While we adjusted for education level, residual differential misclassification cannot be excluded. Fourth, the study population comprised Chinese adults aged 45 years and older, and generalizability to younger populations or other ethnic groups requires further investigation. Fifth, as an observational study, residual confounding by unmeasured factors (such as physical activity levels, dietary patterns, and unmeasured medications) cannot be excluded despite comprehensive adjustment. Sixth, metabolic indices were calculated from single baseline measurements and do not capture temporal changes or cumulative exposure effects, which may be more informative for cardiovascular risk prediction as suggested by Yang et al. and Ma et al.[ 15 , 30 ] Seventh, the Kaplan–Meier survival data and RCS dose–response analyses are presented in Additional files (Tables S4–S5); some discordance between unadjusted KM results and adjusted Cox model findings is expected and reflects differences in statistical power and covariate adjustment, as detailed in Additional file 6. Eighth, the overall interaction P-values (0.056–0.071) did not reach the conventional 0.05 threshold and should be interpreted with caution; however, the convergence of consistent directional patterns across all four endpoints, statistically significant pairwise comparisons, and biologically plausible mechanisms collectively support genuine effect modification. The three-group omnibus test has inherently lower power with unbalanced group sizes (NGT n = 2,916 vs. T2D n = 971). Similarly, paired bootstrap C-index comparisons confirmed directional consistency of the group-switching phenomenon but did not yield statistically significant differences, likely reflecting small absolute ΔC-index values (0.001–0.006); larger studies are needed for definitive confirmation. Conclusions In this large prospective cohort from CHARLS with up to 9 years of follow-up, metabolic status modifies the predictive value of CTI-WHtR for MACCE in both magnitude and functional form. CTI-WHtR demonstrates robust, linear, and independent predictive value in normoglycemic individuals, while the dose–response relationship transforms to an inverted-U shape in type 2 diabetes. A group-switching phenomenon was observed between CTI-based and TyG-based indices across glycemic strata. These findings establish three principles: (1) the early screening value window for CTI-WHtR lies in the normoglycemic stage; (2) metabolic-status-specific index selection should guide cardiovascular risk stratification; and (3) non-linear dose–response modeling should be routinely employed when evaluating metabolic indices across glycemic strata. Abbreviations BMI: Body mass index; CHARLS: China Health and Retirement Longitudinal Study; CI: Confidence interval; CKD: Chronic kidney disease; CRP: C-reactive protein; CTI: C-reactive protein–triglyceride–glucose index; CVD: Cardiovascular disease; eGFR: Estimated glomerular filtration rate; FDR: False discovery rate; FPG: Fasting plasma glucose; HbA1c: Glycated hemoglobin; HDL-C: High-density lipoprotein cholesterol; HR: Hazard ratio; IR: Insulin resistance; KM: Kaplan–Meier; LDL-C: Low-density lipoprotein cholesterol; MACCE: Major adverse cardiovascular and cerebrovascular events; NGT: Normal glucose tolerance; RCS: Restricted cubic spline; SD: Standard deviation; SMD: Standardized mean difference; STROBE: Strengthening the Reporting of Observational Studies in Epidemiology; T2D: Type 2 diabetes; TG: Triglycerides; TyG: Triglyceride-glucose index; VIF: Variance inflation factor; WC: Waist circumference; WHtR: Waist-to-height ratio Declarations Ethics approval and consent to participate This study used publicly available data from the China Health and Retirement Longitudinal Study (CHARLS). The original CHARLS study was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants provided written informed consent. The present analysis of de-identified data did not require additional ethical approval. Consent for publication Not applicable. Availability of data and materials The CHARLS data are publicly available and can be accessed from the CHARLS project website (https://charls.pku.edu.cn/en/). The analytical code used in this study is available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the National Natural Science Foundation of China (Grant number: 82411540241), the Science and Technology Project of Xiamen Medical College (Grant number: K2023-09), and the 2024 Fujian Province Science and Technology Program Project (Grant number: 2024048). The funders had no role in the design of the study, collection, analysis, and interpretation of data, or in writing the manuscript. Authors’ contributions XZL performed the statistical analysis and drafted the initial manuscript. TML assisted with data analysis and critically reviewed and revised the manuscript. YTM and LSZ critically reviewed and revised the manuscript. GY conceived and designed the study, supervised the research, critically reviewed the results of analyses, and reviewed and revised the manuscript. All authors were responsible for data interpretation and approved the final draft of the manuscript. GY is the guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. Acknowledgements The authors gratefully acknowledge the CHARLS research team and all participants for their contributions to data collection. References Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. J Am Coll Cardiol . 2020;76(25):2982–3021. The Writing Committee of the Report on Cardiovascular Health and Diseases in China. Report on cardiovascular health and diseases in China 2022: an updated summary. Chinese Circulation Journal . 2023;38(6):583–612. Kip KE, Hollabaugh K, Marroquin OC, Williams DO. The problem with composite end points in cardiovascular studies: the story of major adverse cardiac events and percutaneous coronary intervention. J Am Coll Cardiol . 2008;51(7):701–707. Bosco E, Hsueh L, McConeghy KW, Gravenstein S, Saade E. Major adverse cardiovascular event definitions used in observational analysis of administrative databases: a systematic review. BMC Med Res Methodol . 2021;21(1):241. Di Pino A, DeFronzo RA. Insulin resistance and atherosclerosis: implications for insulin-sensitizing agents. Endocr Rev . 2019;40(6):1447–1467. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord . 2008;6(4):299–304. Yin JL, Yang J, Song XJ, Qin X, Chang YJ, Chen X, et al. Triglyceride-glucose index and health outcomes: an umbrella review of systematic reviews with meta-analyses of observational studies. Cardiovasc Diabetol . 2024;23(1):177. Liu H, Wang L, Wang H, Hao X, Du Z, Li C, et al. The association of triglyceride-glucose index with major adverse cardiovascular and cerebrovascular events after acute myocardial infarction: a meta-analysis of cohort studies. Nutr Diabetes . 2024;14(1):42. Rao X, Xin Z, Yu Q, Feng L, Shi Y, Tang T, et al. Triglyceride-glucose-body mass index and the incidence of cardiovascular diseases: a meta-analysis of cohort studies. Cardiovasc Diabetol . 2025;24(1):34. Cui C, Liu L, Qi Y, Han N, Xu H, Wang Z, et al. Joint association of TyG index and high sensitivity C-reactive protein with cardiovascular disease: a national cohort study. Cardiovasc Diabetol . 2024;23(1):185. Huo G, Tang Y, Liu Z, Cao J, Yao Z, Zhou D. Association between C-reactive protein-triglyceride glucose index and stroke risk in different glycemic status: insights from the China Health and Retirement Longitudinal Study (CHARLS). Cardiovasc Diabetol . 2025;24(1):142. Ou H, Wei M, Li X, Xia X. C-reactive protein-triglyceride glucose index in evaluating cardiovascular disease and all-cause mortality incidence among individuals across stages 0–3 of cardiovascular–kidney–metabolic syndrome: a nationwide prospective cohort study. Cardiovasc Diabetol . 2025;24(1):296. Chen Y, Jia W, Guo J, Yang H, Sheng X, Wei L, et al. Association between the C-reactive protein-triglyceride glucose index and new-onset coronary heart disease among metabolically heterogeneous individuals. Cardiovasc Diabetol . 2025;24(1):119. Zhang L, Li S, Liu D, Gui J, Hu J, Wang Q, et al. The relationship between C-reactive protein-triglyceride-glucose index and cardiovascular disease: insights from the China Health and Retirement Longitudinal Study (CHARLS). Cardiovasc Diabetol . 2025;24(1). doi:10.1186/s12933-025-02960-w. Ma X, Ma X, Wang Y, Qiu G, Zhang C. Associations of cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index with incident cardiovascular disease in middle-aged and older Chinese adults: a nationwide cohort study. Cardiovasc Diabetol . 2025;24(1):303. Ni G, Chen Z, Zhu A, Cheang I, Zhu X, Fu Y, et al. Association between C-reactive protein-triglyceride glucose index (CTI) and cardiovascular and all-cause mortality risk among the elderly population: insights from three datasets. Clin Res Cardiol . 2026. doi:10.1007/s00392-025-02830-1. Xu X, Liu J, Sun Y, Sun P, Yu X. The association between the C-reactive protein-triglyceride-glucose index and cardiovascular diseases: a cohort study using data from the China Health and Retirement Longitudinal Study 2011–2020. PLoS One . 2025;20(12):e0335916. doi:10.1371/journal.pone.0335916. Yue Y, Li P, Sun Z, Murayama R, Li Z, Hashimoto K, et al. The predictive value of C-reactive protein-triglycerides-glucose index-waist-to-height ratio for stroke: a nationwide cohort study. Cardiovasc Diabetol . 2025;24(1):398. American Diabetes Association Professional Practice Committee. 2. Diagnosis and classification of diabetes: Standards of Care in Diabetes—2024 . Diabetes Care . 2024;47(Suppl 1):S20–S42. Zhang J, Zhan Q, Deng Z, Lin L, Feng Z, He H, et al. Does diabetes modify the triglyceride–glucose index associated with cardiovascular events and mortality? A meta-analysis of 50 cohorts involving 7,239,790 participants. Cardiovasc Diabetol . 2025;24(1):42. Xu S, Zhang Z, Li J, Ding Y, Chen Y, Zhou Y, et al. Does diabetes status modify the association between the triglyceride-glucose index and major adverse cardiovascular events in patients with coronary heart disease? A systematic review and meta-analysis. Cardiovasc Diabetol . 2025;24(1):317. 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(1):61–68. Ye Z, Xie E, Jiao S, Gao Y, Li P, Tu Y, et al. Triglyceride glucose index exacerbates the risk of cardiovascular disease due to diabetes: evidence from CHARLS. BMC Cardiovasc Disord . 2022;22:236. VanderWeele TJ, Shpitser I. On the definition of a confounder. Ann Stat . 2013;41(1):196–220. Li C, Qi Q, Li W, Zhang X, Li L, Deng J, et al. Triglyceride-glucose index predicts major adverse cardiovascular and cerebrovascular events in non-diabetic individuals. Balkan Med J . 2025;42(4):339–346. doi:10.4274/balkanmedj.galenos.2025.2025-2-109. Liu J, Kang J, Liang P, Song Z, Wu H. The association between triglyceride-glucose index and all-cause and cardiovascular mortality according to different glucose metabolism status. Cardiovasc Diabetol . 2025;24(1):367. Scott DA, Ponir C, Shapiro MD, Chevli PA. Associations between insulin resistance indices and subclinical atherosclerosis: a contemporary review. Am J Prev Cardiol . 2024;18:100676. Libby P. The changing landscape of atherosclerosis. Nature . 2021;592(7855):524–533. Hong J, Zhang R, Tang H, Wu S, Chen Y, Tan X. Comparison of triglyceride glucose index and modified triglyceride glucose indices in predicting cardiovascular diseases incidence among populations with cardiovascular-kidney-metabolic syndrome stages 0–3: a nationwide prospective cohort study. Cardiovasc Diabetol . 2025;24(1). doi:10.1186/s12933-025-02662-3. Yang Y, Liu A. Associations of cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index with incident stroke in middle-aged and older Chinese adults: a longitudinal analysis based on CHARLS. Cardiovasc Diabetol . 2025;24(1):386. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1TableS1STROBE.docx Additional file 1: Table S1. STROBE Statement—Checklist of items that should be included in reports of cohort studies, with page numbers corresponding to the present manuscript. Additionalfile2TableS2.xlsx Additional file 2: Table S2. Comparison of baseline characteristics between included participants (n=6,993) and excluded participants (n=10,714), with standardized mean differences (SMD). Additionalfile3TableS3.xlsx Additional file 3: Table S3. Paired bootstrap comparisons (B=1,000) of Harrell’s C-index between matched CTI-derived and TyG-derived metabolic indices for MACCE prediction across three glycemic groups under the fully adjusted model (Model 3). Note: C-index values and sample sizes are consistent with Table 4; Model 3 HRs in Tables 2 and 4 use the larger primary sample (see table footnotes for details). Additionalfile4TableS4KMSummary.xlsx Additional file 4: Table S4. Summary of Kaplan–Meier survival data for MACCE by within-group CTI-WHtR quartiles (Q1–Q4), stratified by glycemic status (NGT, prediabetes, and T2D). The table presents event counts, event rates, quartile-specific event distributions, log-rank P-values, and curve separation patterns across 9 years of follow-up. P-values are from log-rank tests (unadjusted). Note that significant log-rank P-values reflect unadjusted between-quartile differences; after full covariate adjustment in Cox regression (Model 3, Table 2), the associations in the prediabetes and T2D groups were attenuated to non-significance, consistent with the “predictive paradox” described in the main text. Additionalfile5TableS5RCSSummary.xlsx Additional file 5: Table S5. Summary of restricted cubic spline (RCS) dose–response analyses for the association between CTI-WHtR (continuous) and cardiovascular endpoints (MACCE, heart disease, stroke, and all-cause death), stratified by glycemic group. This table integrates the former Table S4 (RCS non-linearity tests) and Table S6 (RCS dose–response summary) into a single comprehensive table; N(KM) and Events(KM) columns are retained for cross-reference with Kaplan–Meier data (Table S4). The table presents sample sizes, event counts, number of knots, P overall , P non-linearity , dose–response shape characterization, reference values, peak HR regions, and corresponding Cox Model 3 HRs per SD. Models are fully adjusted (Model 3: age, sex, smoking, alcohol, education, marital status, residence, hypertension, LDL-C, HDL-C, lipid-lowering medication, CKD, BMI). RCS used 4 knots at the 5th, 35th, 65th, and 95th percentiles (3 df). In the NGT group, significant linear associations were observed across all endpoints (all P non-linearity >0.25). In the prediabetes group, no significant overall association was found for MACCE, heart disease, or stroke. In the T2D group, significant non-linear (inverted-U-shaped) associations were detected for MACCE (P non-linearity =0.017), stroke (P non-linearity =0.017), and all-cause death (P non-linearity =0.023). Additional file 5: Table S5 (continued). The table includes RCS dose–response results for all four endpoints (MACCE, heart disease, stroke, and all-cause death) across three glycemic groups. Model specifications are identical to those described above for Table S5. Additionalfile6ExtendedDiscussion.docx Additional file 6: Extended Discussion. Extended discussion content condensed from the main manuscript, including: (1) detailed implications of the inverted-U-shaped dose–response in the T2D group, (2) extended analysis of missing data mechanism and selection bias, and (3) additional methodological considerations regarding Kaplan–Meier/RCS discordance, interaction P-value interpretation, and bootstrap C-index comparison interpretation. Cite Share Download PDF Status: Published Journal Publication published 26 Apr, 2026 Read the published version in Cardiovascular Diabetology → Version 1 posted Editorial decision: Revision requested 21 Mar, 2026 Reviews received at journal 21 Mar, 2026 Reviews received at journal 18 Mar, 2026 Reviews received at journal 17 Mar, 2026 Reviews received at journal 14 Mar, 2026 Reviews received at journal 07 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers agreed at journal 21 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers invited by journal 20 Feb, 2026 Editor assigned by journal 20 Feb, 2026 Submission checks completed at journal 20 Feb, 2026 First submitted to journal 20 Feb, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8922730","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595655662,"identity":"4cf66f74-1315-4dab-9aeb-b5fc3836f7f4","order_by":0,"name":"Xuanzhe Li","email":"","orcid":"","institution":"Xiamen Haicang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuanzhe","middleName":"","lastName":"Li","suffix":""},{"id":595655663,"identity":"a5e7956a-d51c-4e0a-96eb-99144270ab8e","order_by":1,"name":"Tingmin Li","email":"","orcid":"","institution":"the Second Affiliated Hospital of Xiamen Medical College","correspondingAuthor":false,"prefix":"","firstName":"Tingmin","middleName":"","lastName":"Li","suffix":""},{"id":595655664,"identity":"df5e4232-669f-41bc-8dfb-aafe30450786","order_by":2,"name":"Yitong Meng","email":"","orcid":"","institution":"the Second Affiliated Hospital of Xiamen Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yitong","middleName":"","lastName":"Meng","suffix":""},{"id":595655665,"identity":"61da3642-480c-4ab6-92af-382d2a394133","order_by":3,"name":"Lishuang Zhang","email":"","orcid":"","institution":"the Second Affiliated Hospital of Xiamen Medical College","correspondingAuthor":false,"prefix":"","firstName":"Lishuang","middleName":"","lastName":"Zhang","suffix":""},{"id":595655667,"identity":"1d9d9c35-99ad-422f-bb3c-d0c35c3e81e7","order_by":4,"name":"Gen Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYLCCDxDKgHgdjDMgqknQwsxDkhb5iORnj23+/ElsYG/eJsFQc4ewFsMbaebGuW0GiQ08x8okGI49I0LLjAQz6dwGoBaJHDMJxobDxGhJ/yZt8QeoRf4NkVrkgYZLM7CBbOEhUosBz5syyd42Y+M2nrRii4RjxNjSnr5N4scfOdl+9sMbb3yoIcaWCwkQBhuISCCsAWhL/wFilI2CUTAKRsGIBgA+7DVXkahpnwAAAABJRU5ErkJggg==","orcid":"","institution":"the Second Affiliated Hospital of Xiamen Medical College","correspondingAuthor":true,"prefix":"","firstName":"Gen","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2026-02-20 05:53:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8922730/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8922730/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12933-026-03181-5","type":"published","date":"2026-04-26T15:59:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103401415,"identity":"f77738ea-0c26-4040-be59-3071d00ad5a6","added_by":"auto","created_at":"2026-02-25 09:26:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1026340,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of participant selection from the CHARLS cohort. A total of 9,025 participants were excluded for baseline CVD or missing key laboratory/anthropometric variables, and a further 1,689 for missing follow-up data, yielding a final analytic sample of 6,993 participants classified into three glycemic groups. NGT, normal glucose tolerance; T2D, type 2 diabetes; FPG, fasting plasma glucose; CVD, cardiovascular disease; MACCE, major adverse cardiovascular and cerebrovascular events.\u003c/p\u003e","description":"","filename":"Figure1Flowchart.png","url":"https://assets-eu.researchsquare.com/files/rs-8922730/v1/7de5c86733923cd9a205639d.png"},{"id":103402030,"identity":"59c9f97a-a90d-4b9b-abfd-e2ba4afb2dd7","added_by":"auto","created_at":"2026-02-25 09:27:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":653673,"visible":true,"origin":"","legend":"\u003cp\u003eCTI-WHtR associations with cardiovascular endpoints by glycemic group (Model 3). Forest plot showing hazard ratios per SD increment in CTI-WHtR for four endpoints (MACCE, heart disease, stroke, all-cause death) across three glycemic groups (NGT, prediabetes, T2D). The x-axis is on a logarithmic scale. Circles represent point estimates of hazard ratios; horizontal lines indicate 95% confidence intervals. The dashed vertical line represents the null value (HR=1.0). Bold P-values indicate statistical significance (P\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"Figure2ForestPlot.png","url":"https://assets-eu.researchsquare.com/files/rs-8922730/v1/6679f7ba0ddad9d0c0eb1119.png"},{"id":103401720,"identity":"b18837a8-e0eb-4fe6-9cb2-e19d9362f69e","added_by":"auto","created_at":"2026-02-25 09:27:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":546150,"visible":true,"origin":"","legend":"\u003cp\u003eC-index comparison of eight metabolic indices across glycemic groups. Dot plot comparing Harrell’s C-index for MACCE prediction among eight metabolic indices in the NGT (blue circles) and T2D (red diamonds) groups under Model 3. The dashed horizontal line separates CTI-based indices (above) from TyG-based indices (below). In the NGT group, CTI-based indices consistently achieved higher C-indices than TyG-based indices. In the T2D group, the pattern reversed: TyG-based indices achieved higher C-indices. Asterisks indicate indices with statistically significant HRs per SD (P\u0026lt;0.05). The prediabetes group is not shown because all indices were non-significant after full adjustment (C-index range: 0.627–0.628).\u003c/p\u003e","description":"","filename":"Figure3CindexDotPlot.png","url":"https://assets-eu.researchsquare.com/files/rs-8922730/v1/e41647f2fdf0411f342292a5.png"},{"id":103401475,"identity":"5834ee1e-6af9-4ffb-a28e-d1798ff2a01b","added_by":"auto","created_at":"2026-02-25 09:26:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2296671,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analyses for CTI-WHtR associations across glycemic groups. Analyses for the association between CTI-WHtR (per SD) and cardiovascular endpoints across three glycemic groups. The x-axis is on a logarithmic scale. Circles represent hazard ratios; horizontal lines represent 95% confidence intervals. The dashed line indicates the null value (HR=1.0). Bold P-values indicate statistical significance (P\u0026lt;0.05). Primary analysis uses Model 3 (fully adjusted including BMI) unless otherwise specified. S9 reports subdistribution hazard ratios (SHR) from the Fine-Gray model treating all-cause death as a competing event for non-fatal endpoints (heart disease and stroke); all other analyses report cause-specific hazard ratios from Cox regression. S8 (restriction to stable prediabetes participants without progression to diabetes) is shown only in the Prediabetes panel, as it applies exclusively to this glycemic group and is not applicable to the NGT or T2D groups.\u003c/p\u003e","description":"","filename":"Figure4SensitivityForest.png","url":"https://assets-eu.researchsquare.com/files/rs-8922730/v1/f643432f4c83e0989169dc67.png"},{"id":107928753,"identity":"57099454-f513-4d9b-a1b1-a644acc94fec","added_by":"auto","created_at":"2026-04-27 16:12:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5368901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8922730/v1/93cd6e72-6769-4039-a5aa-6688614dd632.pdf"},{"id":103401453,"identity":"ffe73c6c-dae5-49e6-946d-1367f2a513a7","added_by":"auto","created_at":"2026-02-25 09:26:48","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":32600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1: Table S1.\u003c/strong\u003e STROBE Statement—Checklist of items that should be included in reports of cohort studies, with page numbers corresponding to the present manuscript.\u003c/p\u003e","description":"","filename":"Additionalfile1TableS1STROBE.docx","url":"https://assets-eu.researchsquare.com/files/rs-8922730/v1/cd149fa10dbfab1b874b5044.docx"},{"id":103507134,"identity":"3470299f-c04a-4f01-90af-db99abed032d","added_by":"auto","created_at":"2026-02-26 13:40:33","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":7642,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 2: Table S2.\u003c/strong\u003e Comparison of baseline characteristics between included participants (n=6,993) and excluded participants (n=10,714), with standardized mean differences (SMD).\u003c/p\u003e","description":"","filename":"Additionalfile2TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8922730/v1/f5f50a204b6925240dfc3c5f.xlsx"},{"id":103401442,"identity":"d6759dae-ac72-4ff5-925d-77fd2f08d9dd","added_by":"auto","created_at":"2026-02-25 09:26:41","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":8561,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 3: Table S3.\u003c/strong\u003e Paired bootstrap comparisons (B=1,000) of Harrell’s C-index between matched CTI-derived and TyG-derived metabolic indices for MACCE prediction across three glycemic groups under the fully adjusted model (Model 3). Note: C-index values and sample sizes are consistent with Table 4; Model 3 HRs in Tables 2 and 4 use the larger primary sample (see table footnotes for details).\u003c/p\u003e","description":"","filename":"Additionalfile3TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8922730/v1/43e8fd3d2ad4e6e1f87e1149.xlsx"},{"id":103401493,"identity":"21dbc680-bd7b-4f76-ad30-98f08c1ae570","added_by":"auto","created_at":"2026-02-25 09:26:52","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":6996,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 4: Table S4.\u003c/strong\u003e Summary of Kaplan–Meier survival data for MACCE by within-group CTI-WHtR quartiles (Q1–Q4), stratified by glycemic status (NGT, prediabetes, and T2D). The table presents event counts, event rates, quartile-specific event distributions, log-rank P-values, and curve separation patterns across 9 years of follow-up. P-values are from log-rank tests (unadjusted). Note that significant log-rank P-values reflect unadjusted between-quartile differences; after full covariate adjustment in Cox regression (Model 3, Table 2), the associations in the prediabetes and T2D groups were attenuated to non-significance, consistent with the “predictive paradox” described in the main text.\u003c/p\u003e","description":"","filename":"Additionalfile4TableS4KMSummary.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8922730/v1/b1b836915c0a2c9630c856c9.xlsx"},{"id":103401470,"identity":"35acdfda-65ac-4433-89d0-a246586cb232","added_by":"auto","created_at":"2026-02-25 09:26:50","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":11956,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 5: Table S5.\u003c/strong\u003e Summary of restricted cubic spline (RCS) dose–response analyses for the association between CTI-WHtR (continuous) and cardiovascular endpoints (MACCE, heart disease, stroke, and all-cause death), stratified by glycemic group. This table integrates the former Table S4 (RCS non-linearity tests) and Table S6 (RCS dose–response summary) into a single comprehensive table; N(KM) and Events(KM) columns are retained for cross-reference with Kaplan–Meier data (Table S4). The table presents sample sizes, event counts, number of knots, P\u003csub\u003eoverall\u003c/sub\u003e, P\u003csub\u003enon-linearity\u003c/sub\u003e, dose–response shape characterization, reference values, peak HR regions, and corresponding Cox Model 3 HRs per SD. Models are fully adjusted (Model 3: age, sex, smoking, alcohol, education, marital status, residence, hypertension, LDL-C, HDL-C, lipid-lowering medication, CKD, BMI). RCS used 4 knots at the 5th, 35th, 65th, and 95th percentiles (3 df). In the NGT group, significant linear associations were observed across all endpoints (all P\u003csub\u003enon-linearity\u003c/sub\u003e \u0026gt;0.25). In the prediabetes group, no significant overall association was found for MACCE, heart disease, or stroke. In the T2D group, significant non-linear (inverted-U-shaped) associations were detected for MACCE (P\u003csub\u003enon-linearity\u003c/sub\u003e=0.017), stroke (P\u003csub\u003enon-linearity\u003c/sub\u003e=0.017), and all-cause death (P\u003csub\u003enon-linearity\u003c/sub\u003e=0.023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 5: Table S5 (continued).\u003c/strong\u003e The table includes RCS dose–response results for all four endpoints (MACCE, heart disease, stroke, and all-cause death) across three glycemic groups. Model specifications are identical to those described above for Table S5.\u003c/p\u003e","description":"","filename":"Additionalfile5TableS5RCSSummary.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8922730/v1/5b037e6f55191347dcf0ebd5.xlsx"},{"id":103401489,"identity":"97975b32-d5cf-4ef3-9639-b0b4813e61cc","added_by":"auto","created_at":"2026-02-25 09:26:52","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":40077,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 6: Extended Discussion.\u003c/strong\u003e Extended discussion content condensed from the main manuscript, including: (1) detailed implications of the inverted-U-shaped dose–response in the T2D group, (2) extended analysis of missing data mechanism and selection bias, and (3) additional methodological considerations regarding Kaplan–Meier/RCS discordance, interaction P-value interpretation, and bootstrap C-index comparison interpretation.\u003c/p\u003e","description":"","filename":"Additionalfile6ExtendedDiscussion.docx","url":"https://assets-eu.researchsquare.com/files/rs-8922730/v1/e075068a374778d7a4e04b64.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolic Status Modifies the Predictive Value of the C-reactive Protein– Triglyceride–Glucose Index–Waist-to-Height Ratio for Major Adverse Cardiovascular and Cerebrovascular Events: A Prospective Cohort Study from CHARLS","fulltext":[{"header":"Research Insights","content":"\u003cp\u003e\u003cstrong\u003eWhat is currently known about this topic?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CTI-WHtR is a recently proposed composite biomarker integrating inflammation, insulin resistance, and central obesity. Prior studies have demonstrated associations between CTI derivatives and cardiovascular events, but whether metabolic status modifies the predictive value of CTI-WHtR for MACCE has not been investigated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is the key research question?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDoes glycemic status (normal glucose tolerance, prediabetes, and type 2 diabetes) modify the magnitude and functional form of the CTI-WHtR\u0026ndash;MACCE association?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is new?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCTI-WHtR demonstrates robust, linear, and independent predictive value for MACCE specifically in normoglycemic individuals. In type 2 diabetes, the dose\u0026ndash;response relationship undergoes a qualitative transformation from linear to inverted-U-shaped. A \u0026ldquo;group-switching\u0026rdquo; phenomenon was observed, with CTI-based indices outperforming TyG-based indices in the NGT group and vice versa in the T2D group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow might this study influence clinical practice?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings suggest that metabolic-status-specific index selection (CTI-based for normoglycemic populations, TyG-based for diabetic populations) should guide cardiovascular risk stratification. Non-linear dose\u0026ndash;response modeling should be routinely employed when evaluating metabolic indices across glycemic strata.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eCardiovascular disease (CVD) remains the leading cause of mortality worldwide, accounting for approximately 17.9\u0026nbsp;million deaths annually.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] China bears a disproportionate burden, with CVD responsible for over 40% of all deaths and its prevalence continuing to rise alongside rapid urbanization, dietary transitions, and population aging.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Major adverse cardiovascular and cerebrovascular events (MACCE)\u0026mdash;a composite endpoint encompassing incident heart disease, stroke, and all-cause death\u0026mdash;serve as the standard measure of cardiovascular burden in epidemiological research.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Early identification of individuals at elevated MACCE risk using accessible and cost-effective biomarkers is therefore a public health priority. Composite biomarkers that integrate multiple pathophysiological pathways have emerged as promising tools for this purpose.\u003c/p\u003e \u003cp\u003eInsulin resistance (IR) has been increasingly recognized as a central mechanism linking metabolic dysfunction to atherosclerotic cardiovascular disease.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] The triglyceride-glucose (TyG) index, calculated as Ln[TG (mg/dL) \u0026times; FPG (mg/dL)/2], was among the first surrogate IR markers validated for cardiovascular risk prediction.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] An umbrella review of meta-analyses confirmed robust associations between TyG and a wide spectrum of cardiovascular outcomes.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Subsequently, anthropometric-modified variants\u0026mdash;TyG-BMI, TyG-waist circumference (TyG-WC), and TyG-waist-to-height ratio (TyG-WHtR)\u0026mdash;were developed to incorporate obesity dimensions and demonstrated incremental predictive value in both Chinese and Western populations.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] More recently, the C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose index (CTI), formulated as 0.412 \u0026times; Ln(CRP) + [Ln(TG \u0026times; FPG)] / 2, was proposed to integrate systemic inflammation with IR, and has shown associations with stroke, coronary heart disease, cardiovascular events, and mortality in both Chinese and international cohorts.[\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Its further combination with WHtR, yielding the CTI-WHtR composite, was designed to simultaneously capture inflammation, IR, and central adiposity, and has recently demonstrated strong predictive value for stroke in a Chinese population.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eA critical but understudied question is whether the predictive efficacy of these composite indices varies across different metabolic states. Glycemic status represents a spectrum from normal glucose tolerance (NGT) through prediabetes to overt type 2 diabetes (T2D), each stage characterized by progressively worsening IR, inflammation, and metabolic dysregulation.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] A recent meta-analysis of 50 cohorts involving over 7.2\u0026nbsp;million participants by Zhang et al. found that diabetes status modified the association between the TyG index and ischemic heart disease as well as all-cause mortality, with stronger associations observed in non-diabetic populations.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Xu et al. similarly reported that diabetes modified TyG-MACCE associations in patients with coronary heart disease.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] However, whether metabolic status similarly modifies the predictive value of CTI-WHtR for MACCE has not been investigated. Huo et al. reported that the association between CTI and stroke risk differed across glycemic strata using CHARLS data, but did not examine CTI-WHtR or MACCE as the composite endpoint.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eTo address this gap, we aimed to examine the association between CTI-WHtR and incident MACCE stratified by three glycemic groups using data from the nationally representative CHARLS with up to 9 years of follow-up. Specifically, we employed a stepwise covariate adjustment strategy to dissect adiposity-related collinearity, tested for multiplicative interaction by metabolic status, conducted head-to-head comparisons of eight metabolic indices across glycemic groups, and performed comprehensive sensitivity analyses.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e This study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cohort studies. The completed STROBE checklist is provided as Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The analytical plan, including the MACCE definition, glycemic group classification, covariate adjustment strategy, and sensitivity analyses, was finalized before data analysis was initiated.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eData were drawn from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative prospective cohort of community-dwelling adults aged 45 years and older.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] CHARLS employed a multistage stratified probability-proportional-to-size sampling design covering 150 counties and 450 communities across 28 provinces in mainland China. Baseline data were collected in 2011, with follow-up waves in 2013, 2015, 2018, and 2020. All participants provided written informed consent, and the study was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015).\u003c/p\u003e \u003cp\u003eFrom the initial 17,707 baseline participants, exclusions were applied sequentially: (1) self-reported history of heart disease or stroke at baseline (n\u0026thinsp;=\u0026thinsp;3,154); (2) missing data for fasting blood glucose, triglycerides, C-reactive protein, waist circumference, height, or BMI among the remaining participants (n\u0026thinsp;=\u0026thinsp;5,871); (3) missing follow-up outcome data (n\u0026thinsp;=\u0026thinsp;1,689). The final analytic sample comprised 6,993 participants (Fig.\u0026nbsp;1). A comparison of baseline characteristics between the 6,993 included and 10,714 excluded participants is provided in Additional file 2: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. While demographic characteristics were broadly comparable (age: 58.8 vs. 59.2 years, SMD\u0026thinsp;=\u0026thinsp;0.044; sex: 53.9% vs. 51.0% female, SMD\u0026thinsp;=\u0026thinsp;0.059), notable imbalances were observed for several key variables: CRP levels were substantially lower in the included group (1.55 vs. 4.67 mg/L, SMD\u0026thinsp;=\u0026thinsp;0.418), as were triglycerides (121.77 vs. 154.62 mg/dL, SMD\u0026thinsp;=\u0026thinsp;0.301), and HDL-cholesterol was higher (52.15 vs. 48.89 mg/dL, SMD\u0026thinsp;=\u0026thinsp;0.214). The included sample also had a higher proportion of rural residents (82.9% vs. 70.8%, SMD\u0026thinsp;=\u0026thinsp;0.287) and a lower prevalence of self-reported hypertension (20.8% vs. 27.0%, SMD\u0026thinsp;=\u0026thinsp;0.144). These differences indicate that the analytic sample was metabolically healthier than the excluded population, and the implications of this selection pattern are addressed in the Limitations (Strengths and limitations).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExposure variables\u003c/h3\u003e\n\u003cp\u003eEight metabolic indices were calculated from baseline laboratory and anthropometric measurements:\u003c/p\u003e \u003cp\u003eCTI\u0026thinsp;=\u0026thinsp;0.412 \u0026times; Ln(CRP [mg/L]) + [Ln(TG [mg/dL] \u0026times; FPG [mg/dL])] / 2\u003c/p\u003e \u003cp\u003eTyG\u0026thinsp;=\u0026thinsp;Ln(TG [mg/dL] \u0026times; FPG [mg/dL] / 2)\u003c/p\u003e \u003cp\u003eCTI-WHtR\u0026thinsp;=\u0026thinsp;CTI \u0026times; WHtR \u0026emsp; TyG-WHtR\u0026thinsp;=\u0026thinsp;TyG \u0026times; WHtR\u003c/p\u003e \u003cp\u003eCTI-BMI\u0026thinsp;=\u0026thinsp;CTI \u0026times; BMI \u0026emsp; TyG-BMI\u0026thinsp;=\u0026thinsp;TyG \u0026times; BMI\u003c/p\u003e \u003cp\u003eCTI-WC\u0026thinsp;=\u0026thinsp;CTI \u0026times; WC \u0026emsp; TyG-WC\u0026thinsp;=\u0026thinsp;TyG \u0026times; WC\u003c/p\u003e \u003cp\u003ewhere WHtR denotes waist-to-height ratio, WC denotes waist circumference (cm), and BMI denotes body mass index (kg/m\u003csup\u003e2\u003c/sup\u003e). All indices were standardized (mean\u0026thinsp;=\u0026thinsp;0, SD\u0026thinsp;=\u0026thinsp;1) within the full sample for comparability in Cox models.\u003c/p\u003e\n\u003ch3\u003eGlycemic group classification\u003c/h3\u003e\n\u003cp\u003eParticipants were classified into three groups based on baseline fasting plasma glucose (FPG) and diabetes history: NGT (FPG\u0026thinsp;\u0026lt;\u0026thinsp;100 mg/dL without diabetes history), prediabetes (100\u0026thinsp;\u0026le;\u0026thinsp;FPG\u0026thinsp;\u0026lt;\u0026thinsp;126 mg/dL without diabetes diagnosis), and T2D (FPG\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL, or self-reported physician-diagnosed diabetes, or current use of glucose-lowering medications).[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] This single-FPG-based classification approach is consistent with prior CHARLS-based studies and has been shown to produce reproducible results in this cohort.\u003c/p\u003e\n\u003ch3\u003eOutcome ascertainment\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint was MACCE, defined as the first occurrence of any of the following: (1) incident heart disease (self-reported new diagnosis of heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems); (2) incident stroke (self-reported new diagnosis); (3) all-cause death (confirmed via household or community informant reports and cross-referenced with death registries). Secondary endpoints included each component individually. Follow-up time was calculated from baseline (2011) to the date of first event or last available follow-up (2020), whichever came first.\u003c/p\u003e \u003cp\u003eThe inclusion of all-cause death (rather than cardiovascular-specific death) in the MACCE composite was a pre-specified design choice based on two considerations. First, CHARLS does not provide systematically adjudicated cause-of-death data, and reliance on informant-reported or registry-based cause of death in a community-dwelling population would introduce substantial misclassification bias, particularly for cardiovascular versus non-cardiovascular causes. Prior CHARLS-based studies have consistently adopted all-cause death in composite cardiovascular endpoints for this reason.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] Second, a substantial proportion of deaths in individuals with metabolic dysfunction are attributable to CVD-related complications that may not be captured as direct \u0026ldquo;cardiovascular death\u0026rdquo; in community-based registries (e.g., sudden cardiac death misclassified as unknown cause, or heart-failure-related death coded as respiratory failure). Using all-cause death avoids underestimating the true cardiovascular mortality burden. The analytical implications of this design choice are addressed in the Discussion (the Discussion).\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eCovariates included age (continuous), sex, education level (illiterate, primary, middle school, high school, college or above), marital status (married/cohabiting vs. other), residence (rural vs. urban), current smoking status, current alcohol consumption, history of hypertension (self-reported or measured systolic BP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or diastolic BP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg), use of lipid-lowering medications, chronic kidney disease (CKD, defined as eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e using the CKD-EPI equation), LDL-cholesterol, HDL-cholesterol, and BMI. Variance inflation factors (VIF) were calculated to assess multicollinearity among covariates; all VIF values were below 5, indicating acceptable collinearity levels. The correlation between WHtR and BMI was r\u0026thinsp;=\u0026thinsp;0.78 in the overall sample (r\u0026thinsp;=\u0026thinsp;0.76 in NGT, r\u0026thinsp;=\u0026thinsp;0.79 in prediabetes, r\u0026thinsp;=\u0026thinsp;0.80 in T2D), confirming substantial but not perfect collinearity between these two adiposity measures.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics were compared across the three glycemic groups using one-way ANOVA for continuous variables and chi-squared tests for categorical variables. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of CTI-WHtR with each endpoint, stratified by glycemic group. A deliberate three-model stepwise covariate adjustment strategy was employed: Model 1 adjusted for age and sex; Model 2 additionally adjusted for smoking, alcohol use, education, marital status, and residence; Model 3 further adjusted for hypertension, LDL-C, HDL-C, lipid-lowering medication, CKD, and BMI. The deliberate inclusion of BMI in the final step was designed to assess the extent to which the WHtR component of CTI-WHtR contributed independent predictive information beyond overall adiposity\u0026mdash;an approach recommended for dissecting shared variance in correlated exposure-covariate pairs.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Due to missing covariate data, the fully adjusted model (Model 3) was based on 6,947 participants (NGT: n\u0026thinsp;=\u0026thinsp;2,891; prediabetes: n\u0026thinsp;=\u0026thinsp;3,090; T2D: n\u0026thinsp;=\u0026thinsp;966), representing 99.3% of the total analytic sample (N\u0026thinsp;=\u0026thinsp;6,993).\u003c/p\u003e \u003cp\u003eCTI-WHtR was analyzed both as a continuous variable (per SD increment) and as quartiles (Q1 as reference). P for linear trend was calculated by entering the median value of each quartile as a continuous variable. Multiplicative interaction between CTI-WHtR (continuous) and glycemic group was tested by including a cross-product term in the pooled Cox model, with P\u003csub\u003einteraction\u003c/sub\u003e reported. Pairwise interaction P-values for prediabetes versus NGT and T2D versus NGT were also computed. Head-to-head comparisons of eight indices used Harrell's C-statistic from Model 3 for discrimination. Paired comparisons of Harrell\u0026rsquo;s C-index between CTI-derived and TyG-derived models were performed using bootstrap resampling (B\u0026thinsp;=\u0026thinsp;1,000, seed\u0026thinsp;=\u0026thinsp;2024). In each bootstrap iteration, both competing models were refitted on the same resampled dataset to preserve the correlation structure between paired C-indices, and the difference in C-index (ΔC) was computed. The 95% confidence interval was obtained using the percentile method, and a two-sided P-value was calculated as twice the proportion of bootstrap ΔC values crossing zero. This approach naturally accounts for the correlation between paired C-indices estimated on the same subjects and is valid for Harrell\u0026rsquo;s C-index under censoring, unlike the DeLong test which is designed for binary-outcome AUC comparisons. Subgroup analyses for the prediabetes group examined effect modification by sex, age (\u0026lt;\u0026thinsp;60 vs. \u0026ge;60), BMI (\u0026lt;\u0026thinsp;24 vs. \u0026ge;24 kg/m\u003csup\u003e2\u003c/sup\u003e), hypertension status, CKD status, and residence.\u003c/p\u003e \u003cp\u003eSensitivity analyses included: (S1) complete case analysis excluding any participants with missing covariates; (S2) exclusion of events within the first 2 years to address reverse causation; (S3) age-and-sex-only adjustment to evaluate baseline association strength; (S4) removal of BMI from the full model to assess the impact of adiposity adjustment; (S5\u0026ndash;S7) separate analyses for each MACCE component (heart disease, stroke, all-cause death); (S8) restriction to stable prediabetes participants without progression to diabetes during follow-up; and (S9) Fine-Gray subdistribution hazard model treating all-cause death as a competing event for non-fatal endpoints (heart disease and stroke). Given the multiple comparisons across 288 analyses (8 indices \u0026times; 3 models \u0026times; 3 groups \u0026times; 4 outcomes), we applied a Benjamini\u0026ndash;Hochberg false discovery rate (FDR) procedure at q\u0026thinsp;=\u0026thinsp;0.05 and report both nominal and FDR-adjusted P-values for key findings. The proportional hazards assumption was verified using scaled Schoenfeld residuals (cox.zph function in R). For each stratified Cox model, both a global goodness-of-fit test and variable-specific tests were performed to assess potential time-dependent changes in regression coefficients; no significant violations were detected (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for both global and individual covariate tests), and visual inspection of scaled Schoenfeld residual plots confirmed no systematic time-dependent trends. All analyses were performed using R version 4.2.2 (survival, survminer, rms, cmprsk packages) and SAS 9.4. Two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for the primary analysis.\u003c/p\u003e \u003cp\u003eNo large language models (LLMs) or artificial intelligence-assisted tools were used in the design, data analysis, or drafting of this manuscript.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the 6,993 participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age was 58.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6 years, 53.9% were female, and 29.5% were illiterate. Participants with T2D were older, had higher BMI, WHtR, FPG, HbA1c, triglycerides, CRP levels, and higher prevalence of hypertension and lipid-lowering medication use compared with the NGT and prediabetes groups (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). All eight metabolic indices increased progressively across the NGT, prediabetes, and T2D groups. The MACCE incidence was 18.8%, 21.5%, and 26.0% in the NGT, prediabetes, and T2D groups, respectively. The mean follow-up was 8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 years, with the T2D group having a slightly shorter follow-up duration (7.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24 years) than the NGT (8.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95 years) and prediabetes (8.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 years) groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Study Participants by Glycemic Status (N\u0026thinsp;=\u0026thinsp;6,993)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNGT (n\u0026thinsp;=\u0026thinsp;2,916)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrediabetes (n\u0026thinsp;=\u0026thinsp;3,106)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT2D (n\u0026thinsp;=\u0026thinsp;971)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;6,993)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.92\u0026thinsp;\u0026plusmn;\u0026thinsp;9.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.17\u0026thinsp;\u0026plusmn;\u0026thinsp;9.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.13\u0026thinsp;\u0026plusmn;\u0026thinsp;9.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.78\u0026thinsp;\u0026plusmn;\u0026thinsp;9.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;60, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/cohabiting, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.55\u0026thinsp;\u0026plusmn;\u0026thinsp;3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.26\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.51\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108.10\u0026thinsp;\u0026plusmn;\u0026thinsp;7.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156.77\u0026thinsp;\u0026plusmn;\u0026thinsp;61.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107.94\u0026thinsp;\u0026plusmn;\u0026thinsp;31.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.28\u0026thinsp;\u0026plusmn;\u0026thinsp;53.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.59\u0026thinsp;\u0026plusmn;\u0026thinsp;74.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152.63\u0026thinsp;\u0026plusmn;\u0026thinsp;92.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121.77\u0026thinsp;\u0026plusmn;\u0026thinsp;71.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.32\u0026thinsp;\u0026plusmn;\u0026thinsp;14.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.20\u0026thinsp;\u0026plusmn;\u0026thinsp;15.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.45\u0026thinsp;\u0026plusmn;\u0026thinsp;15.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.15\u0026thinsp;\u0026plusmn;\u0026thinsp;15.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.70\u0026thinsp;\u0026plusmn;\u0026thinsp;31.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121.19\u0026thinsp;\u0026plusmn;\u0026thinsp;35.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119.11\u0026thinsp;\u0026plusmn;\u0026thinsp;36.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118.19\u0026thinsp;\u0026plusmn;\u0026thinsp;34.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.57\u0026thinsp;\u0026plusmn;\u0026thinsp;14.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.94\u0026thinsp;\u0026plusmn;\u0026thinsp;14.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.49\u0026thinsp;\u0026plusmn;\u0026thinsp;15.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.42\u0026thinsp;\u0026plusmn;\u0026thinsp;14.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTI-WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid-lowering medication, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60), %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMACCE, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e547 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e668 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e252 (26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,467 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e426 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e488 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e177 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,091 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e255 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e529 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause death, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e305 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e786 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up time (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n (%). P-values from one-way ANOVA for continuous variables and chi-squared tests for categorical variables. NGT, normal glucose tolerance; T2D, type 2 diabetes; FPG, fasting plasma glucose; TG, triglycerides; CRP, C-reactive protein; WHtR, waist-to-height ratio; CTI, C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose index; TyG, triglyceride-glucose index; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; MACCE, major adverse cardiovascular and cerebrovascular events.\u003c/p\u003e \u003cp\u003e[Figure 1 \u0026mdash; Insert image file here (TIFF, \u0026ge;\u0026thinsp;300 dpi)]\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e Flow diagram of participant selection from the CHARLS cohort. A total of 9,025 participants were excluded for baseline CVD or missing key laboratory/anthropometric variables, and a further 1,689 for missing follow-up data, yielding a final analytic sample of 6,993 participants classified into three glycemic groups. NGT, normal glucose tolerance; T2D, type 2 diabetes; FPG, fasting plasma glucose; CVD, cardiovascular disease; MACCE, major adverse cardiovascular and cerebrovascular events.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrimary analysis: CTI-WHtR and MACCE by glycemic group\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the associations between CTI-WHtR and MACCE across three glycemic groups and three adjustment models. In the NGT group, CTI-WHtR was consistently and significantly associated with MACCE across all models. After full adjustment (Model 3, n\u0026thinsp;=\u0026thinsp;2,891), each SD increment in CTI-WHtR was associated with a 26% increased risk of MACCE (HR 1.26, 95% CI 1.11\u0026ndash;1.43, P\u0026thinsp;=\u0026thinsp;0.0003). The quartile analysis revealed a significant dose\u0026ndash;response relationship (Q4 vs. Q1: HR 1.34, 95% CI 1.00\u0026ndash;1.81, P\u0026thinsp;=\u0026thinsp;0.051; P-trend\u0026thinsp;=\u0026thinsp;0.018). Notably, the HR decreased only modestly from Model 1 (HR 1.37) to Model 3 (HR 1.26) upon BMI adjustment, indicating that CTI-WHtR retained substantial independent predictive information beyond adiposity in the NGT group.\u003c/p\u003e \u003cp\u003eIn contrast, the association was substantially attenuated in the prediabetes group after full adjustment. The per-SD HR decreased from 1.19 (95% CI 1.10\u0026ndash;1.29, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in Model 1 to 1.04 (95% CI 0.94\u0026ndash;1.16, P\u0026thinsp;=\u0026thinsp;0.43) in Model 3, and the Q4-versus-Q1 HR decreased from 1.64 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) to 1.21 (P\u0026thinsp;=\u0026thinsp;0.20), indicating that the predictive signal was largely explained by covariates, particularly BMI. In the T2D group, a similar pattern of attenuation was observed: the per-SD HR decreased from 1.31 (Model 1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) to 1.12 (Model 3, P\u0026thinsp;=\u0026thinsp;0.17), and the Q4-versus-Q1 HR decreased from 2.44 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) to 1.60 (P\u0026thinsp;=\u0026thinsp;0.064; P-trend\u0026thinsp;=\u0026thinsp;0.082). Notably, the T2D Model 3 Q4 HR of 1.60 (95% CI 0.97\u0026ndash;2.62) approached significance, suggesting a possible residual dose\u0026ndash;response relationship that was underpowered in this smaller subgroup (n\u0026thinsp;=\u0026thinsp;966, 252 events).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation Between CTI-WHtR and MACCE Stratified by Glycemic Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR per SD (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4 vs Q1 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eNGT\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,916; 547 events)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37 (1.24\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.79 (1.40\u0026ndash;2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.38 (1.26\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.82 (1.42\u0026ndash;2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26 (1.11\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.34 (1.00\u0026ndash;1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ePrediabetes\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3,106; 668 events)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19 (1.10\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.64 (1.30\u0026ndash;2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19 (1.10\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.63 (1.29\u0026ndash;2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.94\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.21 (0.91\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eT2D\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;971; 252 events)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.31 (1.17\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.44 (1.66\u0026ndash;3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.32 (1.17\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.46 (1.67\u0026ndash;3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12 (0.95\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.60 (0.97\u0026ndash;2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel 1: adjusted for age and sex. Model 2: Model 1\u0026thinsp;+\u0026thinsp;smoking, alcohol use, education, marital status, residence. Model 3: Model 2\u0026thinsp;+\u0026thinsp;hypertension, LDL-C, HDL-C, lipid-lowering medication, CKD, BMI. \u003csup\u003e\u0026dagger;\u003c/sup\u003eModel 3 sample sizes: NGT n\u0026thinsp;=\u0026thinsp;2,891; Prediabetes n\u0026thinsp;=\u0026thinsp;3,090; T2D n\u0026thinsp;=\u0026thinsp;966 (reduced due to missing covariates). HR, hazard ratio; CI, confidence interval; SD, standard deviation; Q, quartile.\u003c/p\u003e \u003cp\u003eThe association between CTI-WHtR and individual MACCE components is shown in Fig.\u0026nbsp;2. The strongest endpoint-specific association was observed for stroke in the NGT group (Model 3: HR 1.71, 95% CI 1.37\u0026ndash;2.14, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), which remained robust after FDR correction. In the prediabetes group, CTI-WHtR showed significant associations with stroke (HR 1.22, 95% CI 1.03\u0026ndash;1.45, P\u0026thinsp;=\u0026thinsp;0.025) and all-cause death (HR 1.33, 95% CI 1.14\u0026ndash;1.56, P\u0026thinsp;=\u0026thinsp;0.0004) but not with MACCE or heart disease. In the T2D group, only the association with all-cause death reached significance (HR 1.26, 95% CI 1.02\u0026ndash;1.56, P\u0026thinsp;=\u0026thinsp;0.032). These findings reveal a consistent pattern of progressive attenuation from NGT to prediabetes to T2D across all endpoints\u0026mdash;a phenomenon we term the \u0026ldquo;predictive paradox,\u0026rdquo; defined as the observation that CTI-WHtR showed the strongest independent predictive value in the metabolically healthiest group (NGT), despite stronger crude (unadjusted) associations in groups with greater metabolic impairment.\u003c/p\u003e \u003cp\u003e[Figure 2 \u0026mdash; Insert image file here (TIFF, \u0026ge;\u0026thinsp;300 dpi)]\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;2\u003c/b\u003e CTI-WHtR associations with cardiovascular endpoints by glycemic group (Model 3). Forest plot showing hazard ratios per SD increment in CTI-WHtR for four endpoints (MACCE, heart disease, stroke, all-cause death) across three glycemic groups (NGT, prediabetes, T2D). The x-axis is on a logarithmic scale. Circles represent point estimates of hazard ratios; horizontal lines indicate 95% confidence intervals. The dashed vertical line represents the null value (HR\u0026thinsp;=\u0026thinsp;1.0). Bold P-values indicate statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInteraction analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of multiplicative interaction tests between glycemic group and CTI-WHtR for each endpoint. Pairwise interaction analysis revealed a statistically significant difference in the CTI-WHtR\u0026ndash;MACCE association between the prediabetes and NGT groups (P\u0026thinsp;=\u0026thinsp;0.027 in Model 3), and between the T2D and NGT groups for the stroke endpoint (P\u0026thinsp;=\u0026thinsp;0.029), confirming that the markedly stronger stroke association in the NGT group (HR 1.71) compared with the T2D group (HR 1.08) was unlikely due to chance. The overall three-group interaction tests yielded P\u003csub\u003einteraction\u003c/sub\u003e values suggestive of effect modification (P\u0026thinsp;=\u0026thinsp;0.071 for MACCE; P\u0026thinsp;=\u0026thinsp;0.056 for stroke in Model 3)\u0026mdash;a pattern consistent with effect modification but reflecting the reduced statistical power inherent in the smaller T2D subgroup (n\u0026thinsp;=\u0026thinsp;971). For heart disease, a similar interaction pattern was observed (overall P\u0026thinsp;=\u0026thinsp;0.077; prediabetes vs. NGT P\u0026thinsp;=\u0026thinsp;0.034). No significant interaction was observed for all-cause death, consistent with the finding that CTI-WHtR predicted mortality across all three glycemic groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInteraction Tests Between Glycemic Group and CTI-WHtR for Cardiovascular Endpoints\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndpoint\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003csub\u003einteraction\u003c/sub\u003e (overall)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP (Prediabetes vs NGT)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP (T2D vs NGT)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMACCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHeart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAll-cause death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eP\u003csub\u003einteraction\u003c/sub\u003e from the multiplicative interaction term (CTI-WHtR \u0026times; glycemic group) in pooled Cox models. Bold values indicate P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Model 1: age\u0026thinsp;+\u0026thinsp;sex. Model 3: fully adjusted including BMI. Note: pairwise interaction tests provide complementary evidence to the overall three-group test, as the latter is susceptible to power limitations when one group (T2D, n\u0026thinsp;=\u0026thinsp;971) is substantially smaller than the others.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHead-to-head comparison of eight metabolic indices\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the fully adjusted (Model 3) HRs and C-index values for all eight metabolic indices predicting MACCE in each glycemic group. In the NGT group, CTI-based indices consistently achieved numerically higher C-index values and stronger statistical significance than TyG-based indices, although the absolute C-index differences were small (0.005\u0026ndash;0.008). The highest C-index was achieved by CTI-WC (0.635), followed by CTI-BMI (0.633), CTI (0.632), and CTI-WHtR (0.632). All four CTI-based indices were statistically significant (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, among TyG-based indices, only TyG-WC reached significance (HR 1.20, P\u0026thinsp;=\u0026thinsp;0.007), while TyG-BMI (HR 1.11, P\u0026thinsp;=\u0026thinsp;0.48), TyG (HR 1.04, P\u0026thinsp;=\u0026thinsp;0.52), and TyG-WHtR (HR 1.12, P\u0026thinsp;=\u0026thinsp;0.11) were non-significant. In the prediabetes group, no index achieved statistical significance after full adjustment, with C-indices clustering narrowly between 0.627 and 0.628. In the T2D group, a numerical reversal was observed: TyG-WC (HR 1.25, C\u0026thinsp;=\u0026thinsp;0.654), TyG-BMI (HR 1.36, C\u0026thinsp;=\u0026thinsp;0.651), and TyG (HR 1.14, C\u0026thinsp;=\u0026thinsp;0.652) emerged as significant predictors, while CTI-WHtR (HR 1.12, P\u0026thinsp;=\u0026thinsp;0.17) and CTI-BMI (HR 1.19, P\u0026thinsp;=\u0026thinsp;0.14) were non-significant. Paired bootstrap comparisons of C-indices between matched CTI and TyG index pairs formally quantified these differences (Additional file 3: Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). In the NGT group, CTI-based indices consistently showed higher C-indices than their TyG counterparts, with the largest difference observed for CTI-BMI versus TyG-BMI (ΔC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.006, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.001 to +\u0026thinsp;0.016, P\u0026thinsp;=\u0026thinsp;0.116) and CTI-WC versus TyG-WC (ΔC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.006, 95% CI 0.000 to +\u0026thinsp;0.014, P\u0026thinsp;=\u0026thinsp;0.050). For the primary exposure, CTI-WHtR achieved a numerically but non-significantly higher C-index than TyG-WHtR (ΔC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.005, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.001 to +\u0026thinsp;0.013, P\u0026thinsp;=\u0026thinsp;0.076). In the T2D group, the direction reversed: TyG-WC showed the largest advantage over CTI-WC (ΔC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.003, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.004 to +\u0026thinsp;0.011, P\u0026thinsp;=\u0026thinsp;0.480), though none reached significance. In the prediabetes group, all differences were negligible (ΔC\u0026thinsp;\u0026le;\u0026thinsp;0.001, all P\u0026thinsp;\u0026gt;\u0026thinsp;0.67). Thus, while the directional pattern of group-switching was consistent across all four index pairs, the absolute C-index differences (0.001\u0026ndash;0.006) were small and did not reach statistical significance, indicating that the group-switching phenomenon represents a descriptive trend requiring confirmation in larger studies. This \u0026ldquo;group-switching\u0026rdquo; pattern is also visualized in Fig.\u0026nbsp;3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHead-to-Head Comparison of Eight Metabolic Indices for MACCE Prediction (Model 3)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNGT (n\u0026thinsp;=\u0026thinsp;2,891; 547 events)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePrediabetes (n\u0026thinsp;=\u0026thinsp;3,090; 664 events)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eT2D (n\u0026thinsp;=\u0026thinsp;966; 252 events)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC-index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC-index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eC-index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCTI-WC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCTI-BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCTI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCTI-WHtR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll HRs are per SD increment. Model 3: fully adjusted including BMI. C-index: Harrell\u0026rsquo;s concordance statistic. Bold P-values indicate statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Indices are ordered by C-index within the NGT group. CTI-based indices are shown in bold; TyG-based indices in regular font. The dashed line separates CTI-based from TyG-based indices. C-index values (Harrell\u0026rsquo;s C via survival::concordance) are computed from the complete-case Model 3 sample (NGT n\u0026thinsp;=\u0026thinsp;2,854; Prediabetes n\u0026thinsp;=\u0026thinsp;3,041; T2D n\u0026thinsp;=\u0026thinsp;953), consistent with the bootstrap sample used in Additional file 3: Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e. HRs are from the primary Model 3 sample (per Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e footnote). Formal paired bootstrap comparisons of C-indices between matched CTI and TyG index pairs (B\u0026thinsp;=\u0026thinsp;1,000) are presented in Additional file 3: Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e; while the directional pattern of CTI superiority in NGT and TyG superiority in T2D was consistent across all index pairs, the absolute ΔC-index differences (0.001\u0026ndash;0.006) were small and did not reach statistical significance at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e[Figure 3 \u0026mdash; Insert image file here (TIFF, \u0026ge;\u0026thinsp;300 dpi)]\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;3C\u003c/b\u003e-index comparison of eight metabolic indices across glycemic groups. Dot plot comparing Harrell\u0026rsquo;s C-index for MACCE prediction among eight metabolic indices in the NGT (blue circles) and T2D (red diamonds) groups under Model 3. The dashed horizontal line separates CTI-based indices (above) from TyG-based indices (below). In the NGT group, CTI-based indices consistently achieved higher C-indices than TyG-based indices. In the T2D group, the pattern reversed: TyG-based indices achieved higher C-indices. Asterisks indicate indices with statistically significant HRs per SD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The prediabetes group is not shown because all indices were non-significant after full adjustment (C-index range: 0.627\u0026ndash;0.628).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis\u003c/h2\u003e \u003cp\u003eTo further explore the non-significant association in the prediabetes group, we performed subgroup analyses for MACCE (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). No subgroup demonstrated a statistically significant association between CTI-WHtR and MACCE in the fully adjusted model, although the elderly subgroup (age\u0026thinsp;\u0026ge;\u0026thinsp;60 years) approached significance (HR 1.16, 95% CI 1.00\u0026ndash;1.34, P\u0026thinsp;=\u0026thinsp;0.053) and the overweight subgroup (BMI\u0026thinsp;\u0026ge;\u0026thinsp;24) showed a trend (HR 1.09, 95% CI 0.96\u0026ndash;1.25, P\u0026thinsp;=\u0026thinsp;0.19). The CKD subgroup (n\u0026thinsp;=\u0026thinsp;92, 14 events) was too small for stable model estimation and is reported without an HR estimate. No significant interaction was detected across any subgroup variable (all P\u003csub\u003einteraction\u003c/sub\u003e\u0026gt;0.10), indicating that the null association in the prediabetes group was consistent across sex, age, BMI, hypertension status, CKD status, and residence rather than being masked by subgroup heterogeneity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup Analysis: CTI-WHtR and MACCE in the Prediabetes Group (Model 3)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR per SD (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.94\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.92\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.88\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026lt;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.82\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16 (1.00\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.92\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.96\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.94\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.84\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo CKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.93\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.89\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14 (0.91\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel 3: fully adjusted. HR per SD increment in CTI-WHtR. All subgroup interaction P-values\u0026thinsp;\u0026gt;\u0026thinsp;0.10. \u003csup\u003e\u0026Dagger;\u003c/sup\u003eCKD subgroup (n\u0026thinsp;=\u0026thinsp;92, 14 events): HR not reported due to insufficient sample size for stable estimation in the fully adjusted model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eThe results of sensitivity analyses are shown in Fig.\u0026nbsp;4. In the NGT group, the association between CTI-WHtR and MACCE remained robust across all sensitivity analyses: complete case analysis (HR 1.27, 95% CI 1.12\u0026ndash;1.45, P\u0026thinsp;=\u0026thinsp;0.0002), exclusion of events within the first 2 years (HR 1.17, 95% CI 1.02\u0026ndash;1.35, P\u0026thinsp;=\u0026thinsp;0.027), and the age-and-sex-only model (HR 1.37, 95% CI 1.24\u0026ndash;1.50, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Removing BMI from the full model yielded a higher HR (1.33 vs. 1.26, 95% CI 1.20\u0026ndash;1.47, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), confirming that BMI adjustment partially but not fully attenuated the association. The stroke endpoint showed the strongest association (HR 1.71, 95% CI 1.37\u0026ndash;2.14, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), while heart disease (HR 1.20, P\u0026thinsp;=\u0026thinsp;0.013) and all-cause death (HR 1.28, P\u0026thinsp;=\u0026thinsp;0.008) were also significant. Fine-Gray competing risk analysis (S9), treating all-cause death as a competing event, yielded consistent subdistribution HRs for heart disease (SHR 1.18, 95% CI 1.03\u0026ndash;1.35, P\u0026thinsp;=\u0026thinsp;0.016) and stroke (SHR 1.49, 95% CI 1.21\u0026ndash;1.83, P\u0026thinsp;=\u0026thinsp;0.0001), confirming that the NGT findings were not driven by competing mortality risk.\u003c/p\u003e \u003cp\u003eIn the prediabetes group, CTI-WHtR showed significant associations only in the simpler models (age\u0026thinsp;+\u0026thinsp;sex only: HR 1.19, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; without BMI: HR 1.11, P\u0026thinsp;=\u0026thinsp;0.009) and for specific endpoints (stroke: HR 1.22, P\u0026thinsp;=\u0026thinsp;0.025; all-cause death: HR 1.33, P\u0026thinsp;=\u0026thinsp;0.0004). Restricting to stable prediabetes participants who did not progress to diabetes during follow-up yielded a null result (HR 1.03, 95% CI 0.92\u0026ndash;1.15, P\u0026thinsp;=\u0026thinsp;0.61). In the T2D group, only all-cause death reached significance in Model 3 (HR 1.26, 95% CI 1.02\u0026ndash;1.56, P\u0026thinsp;=\u0026thinsp;0.032), but removal of BMI from the model restored significance for MACCE (HR 1.23, 95% CI 1.09\u0026ndash;1.39, P\u0026thinsp;=\u0026thinsp;0.0008), further supporting the collinearity hypothesis. Fine-Gray competing risk analysis (S9) confirmed these null findings: in the prediabetes group, subdistribution HRs for heart disease (SHR 1.00, P\u0026thinsp;=\u0026thinsp;0.95) and stroke (SHR 1.15, P\u0026thinsp;=\u0026thinsp;0.16) were non-significant; in the T2D group, SHRs for heart disease (SHR 0.98, P\u0026thinsp;=\u0026thinsp;0.86) and stroke (SHR 1.01, P\u0026thinsp;=\u0026thinsp;0.95) were similarly null.\u003c/p\u003e \u003cp\u003e[Figure 4 \u0026mdash; Insert image file here (TIFF, \u0026ge;\u0026thinsp;300 dpi)]\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;4\u003c/b\u003e Sensitivity analyses for CTI-WHtR associations across glycemic groups. Analyses for the association between CTI-WHtR (per SD) and cardiovascular endpoints across three glycemic groups. The x-axis is on a logarithmic scale. Circles represent hazard ratios; horizontal lines represent 95% confidence intervals. The dashed line indicates the null value (HR\u0026thinsp;=\u0026thinsp;1.0). Bold P-values indicate statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Primary analysis uses Model 3 (fully adjusted including BMI) unless otherwise specified. S9 reports subdistribution hazard ratios (SHR) from the Fine-Gray model treating all-cause death as a competing event for non-fatal endpoints (heart disease and stroke); all other analyses report cause-specific hazard ratios from Cox regression. S8 (restriction to stable prediabetes participants without progression to diabetes) is shown only in the Prediabetes panel, as it applies exclusively to this glycemic group and is not applicable to the NGT or T2D groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal findings\u003c/h2\u003e \u003cp\u003eTo our knowledge, this is the first study to systematically examine how metabolic status modifies the predictive value of CTI-WHtR for MACCE in a large, nationally representative cohort with nearly a decade of prospective follow-up. Four key findings emerged. First, CTI-WHtR independently predicted MACCE in the NGT group after comprehensive covariate adjustment including BMI (HR 1.26, 95% CI 1.11\u0026ndash;1.43, P\u0026thinsp;=\u0026thinsp;0.0003), but this association was attenuated to non-significance in the prediabetes and T2D groups\u0026mdash;a phenomenon we term the \u0026ldquo;predictive paradox.\u0026rdquo; Second, formal multiplicative interaction testing provided evidence of effect modification, with overall P-interaction values suggestive of modification (P\u0026thinsp;=\u0026thinsp;0.071 for MACCE; P\u0026thinsp;=\u0026thinsp;0.056 for stroke) corroborated by statistically significant pairwise comparisons (prediabetes vs. NGT P\u0026thinsp;=\u0026thinsp;0.027 for MACCE; T2D vs. NGT P\u0026thinsp;=\u0026thinsp;0.029 for stroke). Third, restricted cubic spline dose\u0026ndash;response analyses revealed a qualitative transformation: the CTI-WHtR\u0026ndash;outcome relationship was consistently linear in the NGT group (all P-non-linearity\u0026thinsp;\u0026gt;\u0026thinsp;0.25) but shifted to a significantly non-linear, inverted-U-shaped pattern in the T2D group (P-non-linearity\u0026thinsp;=\u0026thinsp;0.017 for MACCE, 0.017 for stroke, 0.023 for all-cause death)\u0026mdash;indicating not merely quantitative weakening but a fundamental change in dose\u0026ndash;response architecture. Fourth, head-to-head comparison of eight metabolic indices revealed a \u0026ldquo;group-switching\u0026rdquo; pattern, with CTI-based indices outperforming TyG-based indices in the NGT group and vice versa in the T2D group. Paired bootstrap comparisons confirmed the directional consistency across all four matched index pairs, although absolute ΔC-index differences (0.001\u0026ndash;0.006) did not reach statistical significance. These findings have important implications for metabolic-status-specific cardiovascular risk stratification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eComparison with existing literature\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e compares the design features and key findings of the present study with prior CHARLS-based studies examining metabolic indices and cardiovascular outcomes. Our study is the first to examine CTI-WHtR specifically, use MACCE as a composite endpoint, employ three-group glycemic stratification, and conduct head-to-head comparison of eight indices with formal interaction testing.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the Present Study with Prior CHARLS-Based Studies on Metabolic Indices and CVD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent Study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuo 2025[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYue 2025[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYe 2022[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYang 2025[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCTI-WHtR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCTI-WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCumulative CTI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMACCE (composite)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCVD composite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycemic stratification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3-group (NGT/Pre/T2D)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3-group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2-group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes (formal)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead-to-head (8 indices)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity analyses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e9 types\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 types\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple comparison correction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFDR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;7,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e~\u0026thinsp;8,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;5,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;7,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKey finding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictive paradox\u0026thinsp;+\u0026thinsp;group-switching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTI predicts stroke in NGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCTI-WHtR best for stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTyG\u0026ndash;CVD modified by DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCumulative CTI \u0026uarr; stroke\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMACCE, major adverse cardiovascular and cerebrovascular events; CTI, C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose index; WHtR, waist-to-height ratio; TyG, triglyceride-glucose index; NGT, normal glucose tolerance; Pre, prediabetes; T2D, type 2 diabetes; FDR, false discovery rate; DM, diabetes mellitus.\u003c/p\u003e \u003cp\u003eOur finding that CTI-WHtR predicts MACCE in normoglycemic individuals is broadly consistent with prior CHARLS-based studies. Huo et al. reported that CTI was significantly associated with stroke risk in those with normal glucose regulation (per-unit HR approximately 1.44) and that this association was attenuated in prediabetes.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Zhang et al. and Xu et al. independently confirmed the association between CTI and incident CVD using CHARLS data, further supporting the predictive utility of CTI-based indices in community-dwelling Chinese adults.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] We extend this observation to the composite MACCE endpoint and the CTI-WHtR composite specifically, and demonstrate for the first time that the attenuation pattern holds across all four MACCE components. The direction of our NGT-group stroke finding (HR 1.71 per SD) is also concordant with Yue et al., who showed that CTI-WHtR was the optimal stroke predictor among CTI derivatives.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe observation that metabolic status modifies TyG\u0026ndash;CVD associations is supported by the recent meta-analysis by Zhang et al. across 50 cohorts involving 7.2\u0026nbsp;million participants, which found stronger TyG\u0026ndash;ischemic heart disease associations in non-diabetic individuals (pooled interaction P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Concordantly, Li et al. demonstrated that the TyG index was an independent predictor of MACCE specifically in non-diabetic individuals.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] Our study extends this finding from TyG to the more comprehensive CTI-WHtR composite at finer granularity (three glycemic groups rather than two). For all-cause mortality, our observation that CTI-WHtR retained predictive value across all three glycemic groups is concordant with the NHANES-based findings by Liu et al.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and with the multi-dataset analysis by Ni et al., who reported significant associations between CTI and both cardiovascular and all-cause mortality in elderly populations.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe predictive paradox\u003c/h2\u003e \u003cp\u003eThe central and most novel finding of this study is the progressive attenuation of CTI-WHtR\u0026rsquo;s predictive value from NGT to prediabetes to T2D for MACCE, heart disease, and stroke, despite stronger crude associations in the T2D group (unadjusted Q4 HR 2.44). We propose three non-mutually-exclusive mechanistic explanations for this paradox.\u003c/p\u003e \u003cp\u003eFirst, the collinearity hypothesis: CTI-WHtR is the product of CTI and WHtR, where WHtR is strongly correlated with BMI (r\u0026thinsp;=\u0026thinsp;0.78 overall; r\u0026thinsp;=\u0026thinsp;0.80 in T2D). When BMI was included in Model 3, the per-SD HR in the prediabetes group decreased from 1.19 to 1.04 (Δ = \u0026minus;0.15, a 79% reduction in excess risk), whereas in the NGT group it decreased from 1.37 to 1.26 (Δ = \u0026minus;0.11, a 30% reduction). This indicates that in the prediabetes and T2D groups, the WHtR component is largely redundant with BMI, whereas in the NGT group CTI-WHtR captures independent residual risk beyond overall adiposity. The sensitivity analysis removing BMI directly supported this: without BMI adjustment, the prediabetes group showed HR 1.11 (P\u0026thinsp;=\u0026thinsp;0.009) and the T2D group HR 1.23 (P\u0026thinsp;=\u0026thinsp;0.0008).\u003c/p\u003e \u003cp\u003eSecond, the pharmacological confounding hypothesis: participants with prediabetes and T2D were more likely to use lipid-lowering medications (3.0% and 8.1%, respectively, vs. 1.7% in NGT), antihypertensives, and glucose-lowering agents. These medications can substantially alter triglycerides, fasting glucose, and inflammatory markers\u0026mdash;the very components of CTI-WHtR\u0026mdash;creating a disconnect between the measured index value and the underlying biological risk.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThird, the competing risk dilution hypothesis: as metabolic disease progresses from NGT to T2D, individuals accumulate additional strong risk factors\u0026mdash;nephropathy, neuropathy, and advanced atherosclerosis\u0026mdash;that independently drive MACCE risk. The marginal predictive contribution of CTI-WHtR is diluted by these competing risk factors that are only partially captured by our covariates. This interpretation is supported by the C-index convergence in the prediabetes group (all eight indices yielded C-indices of 0.627\u0026ndash;0.628), suggesting limited discriminatory capacity when the baseline risk profile is homogeneously elevated. Notably, our Fine-Gray competing risk analysis (S9) demonstrated that accounting for death as a competing event did not materially alter the subdistribution hazard ratios for heart disease or stroke in any glycemic group, suggesting that the attenuation in the prediabetes and T2D groups reflects genuine effect dilution rather than an artifact of differential mortality.\u003c/p\u003e \u003cp\u003eBeyond these mechanistic explanations, RCS dose\u0026ndash;response analyses (Additional file 5: Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e) revealed a finding of potentially greater conceptual significance: the predictive paradox reflects not simply an attenuation of effect size but a fundamental transformation in the dose\u0026ndash;response architecture itself. In the NGT group, the association between CTI-WHtR and all four endpoints was unambiguously linear and monotonic (P-non-linearity\u0026thinsp;=\u0026thinsp;0.260\u0026ndash;0.990), with hazard ratios increasing proportionally across the full exposure range. This linearity validates the per-SD HR as a faithful summary of the true dose\u0026ndash;response relationship in normoglycemic individuals.\u003c/p\u003e \u003cp\u003eIn stark contrast, the T2D group exhibited statistically significant non-linearity for three of four endpoints: MACCE (P-non-linearity\u0026thinsp;=\u0026thinsp;0.017), stroke (P-non-linearity\u0026thinsp;=\u0026thinsp;0.017), and all-cause death (P-non-linearity\u0026thinsp;=\u0026thinsp;0.023). The RCS curves consistently demonstrated an inverted-U-shaped (dome-shaped) dose\u0026ndash;response pattern, with hazard ratios rising steeply in the lower-to-middle CTI-WHtR range, peaking at approximately 3.0, and then declining at higher values. This finding carries two immediate implications. First, it reframes the non-significant per-SD hazard ratios in the T2D group: they likely reflect the mathematical artifact of fitting a linear model to a genuinely non-linear relationship, where the upward and downward limbs of the inverted-U partially cancel each other, yielding a falsely attenuated linear slope. Second, it suggests a \u0026ldquo;risk saturation threshold\u0026rdquo; in diabetic populations, beyond which further metabolic deterioration paradoxically fails to confer additional cardiovascular risk. More broadly, this demonstrates that metabolic status modifies not only the magnitude but also the functional form of biomarker\u0026ndash;outcome associations\u0026mdash;a dimension of effect modification largely unrecognized in the metabolic index literature. Extended discussion of these implications is provided in Additional file 6.\u003c/p\u003e \u003cp\u003eIt is important to note that the MACCE definition includes all-cause death, which demonstrated significant associations with CTI-WHtR across all three glycemic groups (NGT HR 1.28, prediabetes HR 1.33, T2D HR 1.26), with no evidence of effect modification (P-interaction\u0026thinsp;=\u0026thinsp;0.990). This indicates that the \u0026ldquo;predictive paradox\u0026rdquo; was primarily driven by the heart disease and stroke components rather than by all-cause death. Had MACCE been defined without all-cause death, the differential attenuation across glycemic groups would likely have been more pronounced, given that all-cause death was the only component showing consistent associations without interaction. This strengthens our conclusion regarding metabolic status modification of CTI-WHtR\u0026rsquo;s predictive value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eThe group-switching phenomenon\u003c/h2\u003e \u003cp\u003eA particularly intriguing finding was the reversal in performance between CTI-based and TyG-based indices across glycemic groups. In the NGT group, all four CTI-based indices achieved significant HRs and higher C-indices (0.632\u0026ndash;0.635) than TyG-based indices (0.627\u0026ndash;0.630), with most TyG indices being non-significant. In the T2D group, TyG-WC (HR 1.25, C\u0026thinsp;=\u0026thinsp;0.654), TyG-BMI (HR 1.36, C\u0026thinsp;=\u0026thinsp;0.651), and TyG (HR 1.14, C\u0026thinsp;=\u0026thinsp;0.652) all achieved significance, while CTI-WHtR and CTI-BMI did not. Paired bootstrap comparisons confirmed directional consistency across all four matched index pairs, although absolute ΔC-index differences were small (0.001\u0026ndash;0.006) and did not reach statistical significance (Additional file 3: Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The CTI-WC versus TyG-WC comparison in the NGT group came closest to significance (ΔC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.006, P\u0026thinsp;=\u0026thinsp;0.050). The convergence of consistent directional patterns across all pairs, despite individually non-significant tests, provides suggestive evidence warranting confirmation in larger cohorts.\u003c/p\u003e \u003cp\u003eWe interpret this pattern through the lens of the evolving pathobiology of atherosclerosis across the metabolic spectrum.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] In the NGT stage, overt glucolipid derangement is minimal (mean TG 105 mg/dL, FPG 92 mg/dL), and the predominant signal distinguishing individuals at elevated cardiovascular risk is subclinical inflammation. CRP, as a marker of this low-grade inflammatory state, adds critical prognostic information that the pure glucose-lipid TyG signal cannot capture. In the T2D stage, glucolipid perturbations are severe (mean TG 153 mg/dL, FPG 157 mg/dL) and dominate the pathophysiological landscape. In this setting, the direct glucose-lipid signal captured by TyG becomes more informative than the inflammation-weighted CTI signal, because the magnitude of glucolipid dysregulation now overwhelms any additional information provided by CRP. This interpretation aligns with the established model wherein early-stage atherosclerosis is driven by endothelial inflammation and immune activation, while advanced disease features lipid core expansion and plaque destabilization driven by metabolic burden.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] Notably, Hong et al. also reported differential predictive performance between TyG and modified TyG indices across cardiovascular-kidney-metabolic syndrome stages, supporting the concept that the relative utility of metabolic indices varies with the severity of metabolic derangement.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eClinical implications\u003c/h2\u003e \u003cp\u003eOur findings have four practical implications. First, CTI-WHtR may serve as an effective and accessible screening tool for cardiovascular risk specifically in the normoglycemic population\u0026mdash;a group that constituted 41.7% of our cohort (n\u0026thinsp;=\u0026thinsp;2,916) and experienced 547 MACCE events, yet is typically not targeted by conventional diabetes-oriented screening programs. CTI-WHtR requires only routine laboratory tests (CRP, triglycerides, fasting glucose) and basic anthropometry (waist circumference, height), making it feasible for community-based screening. Second, the group-switching phenomenon suggests that a one-size-fits-all approach to metabolic index selection is suboptimal. Clinicians and public health programs may benefit from adopting metabolic-status-specific screening strategies: CTI-based indices (particularly CTI-WC, C\u0026thinsp;=\u0026thinsp;0.635) for normoglycemic populations and TyG-based indices (particularly TyG-WC, C\u0026thinsp;=\u0026thinsp;0.654) for those with established diabetes. Third, the discovery of an inverted-U-shaped dose\u0026ndash;response in the T2D group carries a direct methodological caution: when evaluating metabolic indices in diabetic populations, relying solely on per-SD hazard ratios from linear Cox models may lead to the erroneous conclusion that no association exists; non-linear modeling (e.g., RCS) should be incorporated as a standard analytical step. The risk saturation threshold identified at approximately CTI-WHtR 3.0 in the T2D group could, if replicated, serve as a clinically actionable cut-point above which additional metabolic deterioration no longer confers incremental cardiovascular risk. Fourth, the finding that no index performed well in the prediabetes group after full adjustment suggests that prediabetes may represent a transitional \u0026ldquo;predictive blind spot\u0026rdquo; where traditional metabolic indices have limited incremental value beyond conventional risk factors, and where novel biomarkers or multi-dimensional risk scores may be needed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study has several notable strengths: (a) a large sample size (N\u0026thinsp;=\u0026thinsp;6,993) from a nationally representative cohort ensuring generalizability to the middle-aged and older Chinese population; (b) nearly 9 years of prospective follow-up with 1,467 MACCE events providing adequate statistical power; (c) systematic comparison of all eight CTI and TyG index variants across three glycemic groups using a unified analytical framework; (d) a deliberately designed stepwise adjustment strategy that allowed dissection of adiposity-related collinearity, supported by quantitative VIF and correlation data; (e) formal multiplicative interaction testing with pairwise comparisons; (f) paired bootstrap C-index comparisons between matched index pairs; (g) nine sensitivity analyses including competing risk approaches, stable-subgroup restriction, and early-event exclusion; and (h) multiple comparison correction using the Benjamini\u0026ndash;Hochberg FDR procedure.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, a substantial proportion of baseline participants (5,871 of 17,707, 33.2%) were excluded due to missing laboratory or anthropometric data, and an additional 1,689 for missing follow-up data, which may introduce selection bias if data were not missing completely at random. The included sample was metabolically healthier than the excluded population (CRP: 1.55 vs. 4.67 mg/L, SMD\u0026thinsp;=\u0026thinsp;0.418; triglycerides: 121.77 vs. 154.62 mg/dL, SMD\u0026thinsp;=\u0026thinsp;0.301; HDL-C: 52.15 vs. 48.89 mg/dL, SMD\u0026thinsp;=\u0026thinsp;0.214), which could limit generalizability to populations with higher inflammatory or metabolic burden. However, demographic variables showed negligible imbalances (age: SMD\u0026thinsp;=\u0026thinsp;0.044; sex: SMD\u0026thinsp;=\u0026thinsp;0.059), and the missingness is plausibly missing at random conditional on observed covariates, as phlebotomy availability in CHARLS was primarily driven by logistics and participant willingness rather than health status. Our complete case sensitivity analysis yielded virtually identical results (NGT: HR 1.27 vs. 1.26). Multiple imputation was not performed due to the complexity of the stratified analytical framework. Extended discussion of the missing data mechanism is provided in Additional file 6.\u003c/p\u003e \u003cp\u003eSecond, glycemic classification was based on a single fasting glucose measurement without oral glucose tolerance testing or repeated HbA1c measurements, which may introduce misclassification bias. However, single-FPG-based classification is standard practice in CHARLS-based studies, and any resulting non-differential misclassification would likely bias associations toward the null, making our NGT findings more conservative.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] Third, MACCE was ascertained through biennial self-report and death registries rather than centrally adjudicated clinical events, which may introduce outcome misclassification. Importantly, the accuracy of self-reported cardiovascular events may vary across education levels and cognitive function, both of which are correlated with glycemic status, raising the possibility of differential outcome misclassification across glycemic strata that could bias interaction estimates in either direction. While we adjusted for education level, residual differential misclassification cannot be excluded. Fourth, the study population comprised Chinese adults aged 45 years and older, and generalizability to younger populations or other ethnic groups requires further investigation.\u003c/p\u003e \u003cp\u003eFifth, as an observational study, residual confounding by unmeasured factors (such as physical activity levels, dietary patterns, and unmeasured medications) cannot be excluded despite comprehensive adjustment. Sixth, metabolic indices were calculated from single baseline measurements and do not capture temporal changes or cumulative exposure effects, which may be more informative for cardiovascular risk prediction as suggested by Yang et al. and Ma et al.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] Seventh, the Kaplan\u0026ndash;Meier survival data and RCS dose\u0026ndash;response analyses are presented in Additional files (Tables S4\u0026ndash;S5); some discordance between unadjusted KM results and adjusted Cox model findings is expected and reflects differences in statistical power and covariate adjustment, as detailed in Additional file 6. Eighth, the overall interaction P-values (0.056\u0026ndash;0.071) did not reach the conventional 0.05 threshold and should be interpreted with caution; however, the convergence of consistent directional patterns across all four endpoints, statistically significant pairwise comparisons, and biologically plausible mechanisms collectively support genuine effect modification. The three-group omnibus test has inherently lower power with unbalanced group sizes (NGT n\u0026thinsp;=\u0026thinsp;2,916 vs. T2D n\u0026thinsp;=\u0026thinsp;971). Similarly, paired bootstrap C-index comparisons confirmed directional consistency of the group-switching phenomenon but did not yield statistically significant differences, likely reflecting small absolute ΔC-index values (0.001\u0026ndash;0.006); larger studies are needed for definitive confirmation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this large prospective cohort from CHARLS with up to 9 years of follow-up, metabolic status modifies the predictive value of CTI-WHtR for MACCE in both magnitude and functional form. CTI-WHtR demonstrates robust, linear, and independent predictive value in normoglycemic individuals, while the dose\u0026ndash;response relationship transforms to an inverted-U shape in type 2 diabetes. A group-switching phenomenon was observed between CTI-based and TyG-based indices across glycemic strata. These findings establish three principles: (1) the early screening value window for CTI-WHtR lies in the normoglycemic stage; (2) metabolic-status-specific index selection should guide cardiovascular risk stratification; and (3) non-linear dose\u0026ndash;response modeling should be routinely employed when evaluating metabolic indices across glycemic strata.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI: Body mass index; CHARLS: China Health and Retirement Longitudinal Study; CI: Confidence interval; CKD: Chronic kidney disease; CRP: C-reactive protein; CTI: C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose index; CVD: Cardiovascular disease; eGFR: Estimated glomerular filtration rate; FDR: False discovery rate; FPG: Fasting plasma glucose; HbA1c: Glycated hemoglobin; HDL-C: High-density lipoprotein cholesterol; HR: Hazard ratio; IR: Insulin resistance; KM: Kaplan\u0026ndash;Meier; LDL-C: Low-density lipoprotein cholesterol; MACCE: Major adverse cardiovascular and cerebrovascular events; NGT: Normal glucose tolerance; RCS: Restricted cubic spline; SD: Standard deviation; SMD: Standardized mean difference; STROBE: Strengthening the Reporting of Observational Studies in Epidemiology; T2D: Type 2 diabetes; TG: Triglycerides; TyG: Triglyceride-glucose index; VIF: Variance inflation factor; WC: Waist circumference; WHtR: Waist-to-height ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eThis study used publicly available data from the China Health and Retirement Longitudinal Study (CHARLS). The original CHARLS study was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants provided written informed consent. The present analysis of de-identified data did not require additional ethical approval.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe CHARLS data are publicly available and can be accessed from the CHARLS project website (https://charls.pku.edu.cn/en/). The analytical code used in this study is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (Grant number: 82411540241), the Science and Technology Project of Xiamen Medical College (Grant number: K2023-09), and the 2024 Fujian Province Science and Technology Program Project (Grant number: 2024048). The funders had no role in the design of the study, collection, analysis, and interpretation of data, or in writing the manuscript.\u003c/p\u003e\n\u003ch3\u003eAuthors’ contributions\u003c/h3\u003e\n\u003cp\u003eXZL performed the statistical analysis and drafted the initial manuscript. TML assisted with data analysis and critically reviewed and revised the manuscript. YTM and LSZ critically reviewed and revised the manuscript. GY conceived and designed the study, supervised the research, critically reviewed the results of analyses, and reviewed and revised the manuscript. All authors were responsible for data interpretation and approved the final draft of the manuscript. GY is the guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors gratefully acknowledge the CHARLS research team and all participants for their contributions to data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global burden of cardiovascular diseases and risk factors, 1990\u0026ndash;2019: update from the GBD 2019 study. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e. 2020;76(25):2982\u0026ndash;3021.\u003c/li\u003e\n\u003cli\u003eThe Writing Committee of the Report on Cardiovascular Health and Diseases in China. Report on cardiovascular health and diseases in China 2022: an updated summary. \u003cem\u003eChinese Circulation Journal\u003c/em\u003e. 2023;38(6):583\u0026ndash;612.\u003c/li\u003e\n\u003cli\u003eKip KE, Hollabaugh K, Marroquin OC, Williams DO. The problem with composite end points in cardiovascular studies: the story of major adverse cardiac events and percutaneous coronary intervention. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e. 2008;51(7):701\u0026ndash;707.\u003c/li\u003e\n\u003cli\u003eBosco E, Hsueh L, McConeghy KW, Gravenstein S, Saade E. Major adverse cardiovascular event definitions used in observational analysis of administrative databases: a systematic review. \u003cem\u003eBMC Med Res Methodol\u003c/em\u003e. 2021;21(1):241.\u003c/li\u003e\n\u003cli\u003eDi Pino A, DeFronzo RA. Insulin resistance and atherosclerosis: implications for insulin-sensitizing agents. \u003cem\u003eEndocr Rev\u003c/em\u003e. 2019;40(6):1447\u0026ndash;1467.\u003c/li\u003e\n\u003cli\u003eSimental-Mend\u0026iacute;a LE, Rodr\u0026iacute;guez-Mor\u0026aacute;n M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. \u003cem\u003eMetab Syndr Relat Disord\u003c/em\u003e. 2008;6(4):299\u0026ndash;304.\u003c/li\u003e\n\u003cli\u003eYin JL, Yang J, Song XJ, Qin X, Chang YJ, Chen X, et al. Triglyceride-glucose index and health outcomes: an umbrella review of systematic reviews with meta-analyses of observational studies. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2024;23(1):177.\u003c/li\u003e\n\u003cli\u003eLiu H, Wang L, Wang H, Hao X, Du Z, Li C, et al. The association of triglyceride-glucose index with major adverse cardiovascular and cerebrovascular events after acute myocardial infarction: a meta-analysis of cohort studies. \u003cem\u003eNutr Diabetes\u003c/em\u003e. 2024;14(1):42.\u003c/li\u003e\n\u003cli\u003eRao X, Xin Z, Yu Q, Feng L, Shi Y, Tang T, et al. Triglyceride-glucose-body mass index and the incidence of cardiovascular diseases: a meta-analysis of cohort studies. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2025;24(1):34.\u003c/li\u003e\n\u003cli\u003eCui C, Liu L, Qi Y, Han N, Xu H, Wang Z, et al. Joint association of TyG index and high sensitivity C-reactive protein with cardiovascular disease: a national cohort study. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2024;23(1):185.\u003c/li\u003e\n\u003cli\u003eHuo G, Tang Y, Liu Z, Cao J, Yao Z, Zhou D. Association between C-reactive protein-triglyceride glucose index and stroke risk in different glycemic status: insights from the China Health and Retirement Longitudinal Study (CHARLS). \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2025;24(1):142.\u003c/li\u003e\n\u003cli\u003eOu H, Wei M, Li X, Xia X. C-reactive protein-triglyceride glucose index in evaluating cardiovascular disease and all-cause mortality incidence among individuals across stages 0\u0026ndash;3 of cardiovascular\u0026ndash;kidney\u0026ndash;metabolic syndrome: a nationwide prospective cohort study. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2025;24(1):296.\u003c/li\u003e\n\u003cli\u003eChen Y, Jia W, Guo J, Yang H, Sheng X, Wei L, et al. Association between the C-reactive protein-triglyceride glucose index and new-onset coronary heart disease among metabolically heterogeneous individuals. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2025;24(1):119.\u003c/li\u003e\n\u003cli\u003eZhang L, Li S, Liu D, Gui J, Hu J, Wang Q, et al. The relationship between C-reactive protein-triglyceride-glucose index and cardiovascular disease: insights from the China Health and Retirement Longitudinal Study (CHARLS). \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2025;24(1). doi:10.1186/s12933-025-02960-w.\u003c/li\u003e\n\u003cli\u003eMa X, Ma X, Wang Y, Qiu G, Zhang C. Associations of cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index with incident cardiovascular disease in middle-aged and older Chinese adults: a nationwide cohort study. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2025;24(1):303.\u003c/li\u003e\n\u003cli\u003eNi G, Chen Z, Zhu A, Cheang I, Zhu X, Fu Y, et al. Association between C-reactive protein-triglyceride glucose index (CTI) and cardiovascular and all-cause mortality risk among the elderly population: insights from three datasets. \u003cem\u003eClin Res Cardiol\u003c/em\u003e. 2026. doi:10.1007/s00392-025-02830-1.\u003c/li\u003e\n\u003cli\u003eXu X, Liu J, Sun Y, Sun P, Yu X. The association between the C-reactive protein-triglyceride-glucose index and cardiovascular diseases: a cohort study using data from the China Health and Retirement Longitudinal Study 2011\u0026ndash;2020. \u003cem\u003ePLoS One\u003c/em\u003e. 2025;20(12):e0335916. doi:10.1371/journal.pone.0335916.\u003c/li\u003e\n\u003cli\u003eYue Y, Li P, Sun Z, Murayama R, Li Z, Hashimoto K, et al. The predictive value of C-reactive protein-triglycerides-glucose index-waist-to-height ratio for stroke: a nationwide cohort study. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2025;24(1):398.\u003c/li\u003e\n\u003cli\u003eAmerican Diabetes Association Professional Practice Committee. 2. Diagnosis and classification of diabetes: \u003cem\u003eStandards of Care in Diabetes\u0026mdash;2024\u003c/em\u003e. \u003cem\u003eDiabetes Care\u003c/em\u003e. 2024;47(Suppl 1):S20\u0026ndash;S42.\u003c/li\u003e\n\u003cli\u003eZhang J, Zhan Q, Deng Z, Lin L, Feng Z, He H, et al. Does diabetes modify the triglyceride\u0026ndash;glucose index associated with cardiovascular events and mortality? A meta-analysis of 50 cohorts involving 7,239,790 participants. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2025;24(1):42.\u003c/li\u003e\n\u003cli\u003eXu S, Zhang Z, Li J, Ding Y, Chen Y, Zhou Y, et al. Does diabetes status modify the association between the triglyceride-glucose index and major adverse cardiovascular events in patients with coronary heart disease? A systematic review and meta-analysis. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2025;24(1):317.\u003c/li\u003e\n\u003cli\u003eZhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). \u003cem\u003eInt J Epidemiol\u003c/em\u003e. 2014;43(1):61\u0026ndash;68.\u003c/li\u003e\n\u003cli\u003eYe Z, Xie E, Jiao S, Gao Y, Li P, Tu Y, et al. Triglyceride glucose index exacerbates the risk of cardiovascular disease due to diabetes: evidence from CHARLS. \u003cem\u003eBMC Cardiovasc Disord\u003c/em\u003e. 2022;22:236.\u003c/li\u003e\n\u003cli\u003eVanderWeele TJ, Shpitser I. On the definition of a confounder. \u003cem\u003eAnn Stat\u003c/em\u003e. 2013;41(1):196\u0026ndash;220.\u003c/li\u003e\n\u003cli\u003eLi C, Qi Q, Li W, Zhang X, Li L, Deng J, et al. Triglyceride-glucose index predicts major adverse cardiovascular and cerebrovascular events in non-diabetic individuals. \u003cem\u003eBalkan Med J\u003c/em\u003e. 2025;42(4):339\u0026ndash;346. doi:10.4274/balkanmedj.galenos.2025.2025-2-109.\u003c/li\u003e\n\u003cli\u003eLiu J, Kang J, Liang P, Song Z, Wu H. The association between triglyceride-glucose index and all-cause and cardiovascular mortality according to different glucose metabolism status. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2025;24(1):367.\u003c/li\u003e\n\u003cli\u003eScott DA, Ponir C, Shapiro MD, Chevli PA. Associations between insulin resistance indices and subclinical atherosclerosis: a contemporary review. \u003cem\u003eAm J Prev Cardiol\u003c/em\u003e. 2024;18:100676.\u003c/li\u003e\n\u003cli\u003eLibby P. The changing landscape of atherosclerosis. \u003cem\u003eNature\u003c/em\u003e. 2021;592(7855):524\u0026ndash;533.\u003c/li\u003e\n\u003cli\u003eHong J, Zhang R, Tang H, Wu S, Chen Y, Tan X. Comparison of triglyceride glucose index and modified triglyceride glucose indices in predicting cardiovascular diseases incidence among populations with cardiovascular-kidney-metabolic syndrome stages 0\u0026ndash;3: a nationwide prospective cohort study. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2025;24(1). doi:10.1186/s12933-025-02662-3.\u003c/li\u003e\n\u003cli\u003eYang Y, Liu A. Associations of cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index with incident stroke in middle-aged and older Chinese adults: a longitudinal analysis based on CHARLS. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2025;24(1):386.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"C-reactive protein–triglyceride–glucose index, waist-to-height ratio, major adverse cardiovascular and cerebrovascular events, metabolic status, insulin resistance, predictive paradox, dose–response, nonlinearity, prospective cohort, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-8922730/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8922730/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose index combined with the waist-to-height ratio (CTI-WHtR) is a novel composite biomarker integrating inflammation, insulin resistance, and central obesity. Whether its predictive value for major adverse cardiovascular and cerebrovascular events (MACCE) varies across metabolic states remains unknown.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e We included 6,993 participants free of cardiovascular disease at baseline from the China Health and Retirement Longitudinal Study (CHARLS, 2011\u0026ndash;2020). Participants were classified into normal glucose tolerance (NGT, n\u0026thinsp;=\u0026thinsp;2,916), prediabetes (n\u0026thinsp;=\u0026thinsp;3,106), and type 2 diabetes (T2D, n\u0026thinsp;=\u0026thinsp;971). Stratified multivariable Cox regression with stepwise covariate adjustment, multiplicative interaction testing, head-to-head comparison of eight metabolic indices, restricted cubic spline (RCS) dose\u0026ndash;response analyses, subgroup analyses, and multiple sensitivity analyses were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOver a mean follow-up of 8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 years, 1,467 MACCE events occurred. After full adjustment including BMI, each standard deviation increase in CTI-WHtR was significantly associated with MACCE in the NGT group (HR 1.26, 95% CI 1.11\u0026ndash;1.43, P\u0026thinsp;=\u0026thinsp;0.0003), but not in the prediabetes (HR 1.04, P\u0026thinsp;=\u0026thinsp;0.43) or T2D group (HR 1.12, P\u0026thinsp;=\u0026thinsp;0.17). Pairwise interaction testing confirmed effect modification (prediabetes vs. NGT P\u0026thinsp;=\u0026thinsp;0.027 for MACCE). RCS analyses revealed that the dose\u0026ndash;response relationship was consistently linear in the NGT group (all P\u003csub\u003enon\u0026minus;linearity\u003c/sub\u003e \u0026gt; 0.25), whereas it shifted to a non-linear inverted-U shape in the T2D group (P\u003csub\u003enon\u0026minus;linearity\u003c/sub\u003e = 0.017 for MACCE). In head-to-head comparisons, CTI-based indices achieved higher C-indices in the NGT group, while TyG-based indices performed better in the T2D group, demonstrating a \u0026ldquo;group-switching\u0026rdquo; phenomenon.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe predictive value of CTI-WHtR for MACCE is modified by metabolic status in both magnitude and functional form. CTI-WHtR demonstrates robust, linear, and independent predictive value in normoglycemic individuals, while the dose\u0026ndash;response relationship transforms to an inverted-U shape in type 2 diabetes. These findings establish that metabolic status should be considered when interpreting both the strength and functional form of metabolic index\u0026ndash;cardiovascular risk associations.\u003c/p\u003e","manuscriptTitle":"Metabolic Status Modifies the Predictive Value of the C-reactive Protein– Triglyceride–Glucose Index–Waist-to-Height Ratio for Major Adverse Cardiovascular and Cerebrovascular Events: A Prospective Cohort Study from CHARLS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 09:23:48","doi":"10.21203/rs.3.rs-8922730/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-21T07:09:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-21T06:18:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-18T15:25:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T07:56:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-14T14:06:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-07T12:47:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T13:12:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238035635803415446100917068716406814420","date":"2026-02-25T11:13:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166073891382993488020921344575881956295","date":"2026-02-24T01:22:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73592823917425724641019312036559850931","date":"2026-02-23T12:48:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29718419464765610173902741936094136952","date":"2026-02-23T08:55:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322068251683585909965727026324291318443","date":"2026-02-21T11:39:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336635053848693310178608491067617258117","date":"2026-02-21T03:09:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20592693390755156843386797490849241908","date":"2026-02-21T02:35:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233387565075845572429509025663792291954","date":"2026-02-21T00:59:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-20T08:28:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-20T07:25:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-20T06:21:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2026-02-20T05:48:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a766574d-cbc5-46ac-a41e-fcfd9fdce065","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:08:40+00:00","versionOfRecord":{"articleIdentity":"rs-8922730","link":"https://doi.org/10.1186/s12933-026-03181-5","journal":{"identity":"cardiovascular-diabetology","isVorOnly":false,"title":"Cardiovascular Diabetology"},"publishedOn":"2026-04-26 15:59:19","publishedOnDateReadable":"April 26th, 2026"},"versionCreatedAt":"2026-02-25 09:23:48","video":"","vorDoi":"10.1186/s12933-026-03181-5","vorDoiUrl":"https://doi.org/10.1186/s12933-026-03181-5","workflowStages":[]},"version":"v1","identity":"rs-8922730","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8922730","identity":"rs-8922730","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}