Predictive Value of Triglyceride-Glucose (TyG) Index and the Modified TyG Index combined with Cardiopulmonary Exercise Testing (CPET) Parameters for Major Adverse Cardiovascular Events (MACE)

Predictive Value of Triglyceride-Glucose (TyG) Index and the Modified TyG Index combined with Cardiopulmonary Exercise Testing (CPET) Parameters for Major Adverse Cardiovascular Events (MACE) | 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 Predictive Value of Triglyceride-Glucose (TyG) Index and the Modified TyG Index combined with Cardiopulmonary Exercise Testing (CPET) Parameters for Major Adverse Cardiovascular Events (MACE) Sicheng Ning, Ning Sun, XingBo Mu, Jia Wang, YanChun Liang, Jian Zhang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8868777/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The TyG index, serving as a convenient alternative marker for IR, has been established as correlating with cardiovascular disease risk. The TyG index also demonstrates robust predictive capacity for cardiovascular outcomes. However, by depending solely on biomarkers, the TyG index fails to reflect critical functional states, including cardiopulmonary reserve. In contrast, CPET has been established as an objective, quantitative functional assessment tool for evaluating cardiorespiratory fitness in patients undergoing PCI, while providing evidence for prognostic risk stratification. The present study aimed to develop the modified TyG indices by combining the TyG index with CPET parameters and to compare their predictive performance for MACE against that of the original TyG index. Method The study used Cox proportional hazards regression to analyze the association and predictive capacity based on hazard ratio and Harrell’s C-index. Mediation analysis was performed to quantify the mediation effect. Result The study enrolled 8,803 ACS patients who underwent CPET after PCI. The mean participant age was 57.39 ± 8.97 years, and 2,032 participants (23.1%) were female. Over a median follow-up of 5.0 years, 1,548 MACE events occurred (17.6%). For each index, including the TyG index and its modified versions (peak VO₂/kg_ TyG, AT VO₂/kg_ TyG, and peak O₂ pulse_ TyG), participants were independently divided into tertiles (T1–T3) to evaluate their respective associations with MACE. Compared with the lowest tertile, the highest tertile was associated with the following HRs for MACE: TyG, 1.169 (95% CI, 1.027–1.330); peak VO₂/kg_ TyG, 0.814 (95% CI, 0.711–0.932); peak O₂ pulse_ TyG, 0.835 (95% CI, 0.727–0.959); and AT VO₂/kg_ TyG, 0.870 (95% CI, 0.766–0.990). The C-index values for peak VO₂/kg_ TyG and AT VO₂/kg_ TyG for MACE were higher than that of the TyG index. Mediation analysis was employed to delineate these relationships. Peak VO₂/kg mediated 17.9% of the association of the TyG index with MACE. In contrast, TyG mediated only 1.5% of the association of peak VO₂/kg with MACE. Conclusion In summary, peak VO₂/kg_ TyG showed significantly improved predictive accuracy MACE risk stratification in ACS patients compared to the traditional TyG index. Cardiac Rehabilitation (CR) Cardiopulmonary Exercise Testing (CPET) Triglyceride-Glucose Index (TyG) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Insulin resistance, one of important pathophysiological mechanism underlying the development and progression of atherosclerotic cardiovascular disease (ASCVD), demonstrates significant clinical value in cardiovascular risk stratification [ 1 – 3 ] . While the hyperinsulinemic-euglycemic clamp (HIEC) remains the gold standard for assessing IR [ 4 ] , its clinical utility is constrained by procedural complexity and high costs. In recent years, the triglyceride-glucose (TyG) index has emerged as a reliable alternative marker for IR in large-scale epidemiological studies, owing to its accessibility and reproducibility [ 5 ] . Previous research shown the TyG index correlates with subclinical atherosclerosis markers such as carotid intima-media thickness (CIMT) and coronary artery calcium score (CACS) and independently predicts cardiovascular risks such as acute coronary syndrome [ 6 – 8 ] . While the TyG index holded significant value as a static biomarker of insulin resistance, its characteristic of being dependent on serum biochemical indicators limits its ability to assess individual functional reserve [ 9 ] . Cardiopulmonary exercise testing (CPET) is an objective method for quantifying cardiopulmonary function. By monitoring key physiological responses such as gas exchange, electrocardiogram (ECG), and hemodynamics during exercise, it aids cardiovascular disease management through diagnosis, prognosis assessment, and tracking treatment effectiveness. Furthermore, it accurately identifies exercise-induced myocardial ischemia, malignant arrhythmias, and abnormal hemodynamic responses, emphasizing its clinical significance in cardiovascular medicine [ 10 – 12 ] . The purpose of this present study is to develop an optimized TyG index through integration of CPET parameters. The study aimed to compare the predictive performance of the conventional versus modified TyG indices for major adverse cardiovascular events (MACE). These findings may provide novel evidence for refining cardiovascular risk stratification models. Methods Study Design and Population This retrospective cohort study enrolled patients who underwent CPET at the General Hospital of Northern Theater Command from March, 2016 to May, 2019. Inclusion criteria were: Patients diagnosed with ACS who successfully underwent PCI. Clinically stable and able to tolerate and complete a standard CPET. Exclusion criteria comprised: Presence of co-existing conditions such as hereditary cardiomyopathy, severe valvular heart disease, end-stage renal disease, or active malignancy. Acute or active medical conditions that could interfere with cardiopulmonary function assessment, including (but not limited to) uncontrolled severe infection within the past 4 weeks, acute pulmonary embolism, or a stroke within the preceding 3 months. The study protocol was approved by the Ethics Committee of General Hospital of Northern Theater Command [Y (2025)444]. A waiver of informed consent was granted due to the retrospective nature of the study and anonymized data processing. This study adhered to the principles of the Declaration of Helsinki. Data Collection and Definitions Baseline clinical characteristics included age, sex, body mass index (BMI), smoking history, alcohol consumption, and comorbidities (e.g., hypertension, diabetes mellitus, prior acute myocardial infarction [AMI]). Hypertension was defined as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg, or current use of antihypertensive therapy. Diabetes mellitus was diagnosed based on fasting blood glucose ≥ 7.0 mmol/L, 2-hour postprandial blood glucose ≥ 11.1 mmol/L, glycated hemoglobin (HbA1c) ≥ 6.5%, or a confirmed diagnosis of diabetes with active treatment. Fasting venous blood samples (after ≥ 8 hours of fasting) collected closest to the CPET date (typically within one week before or after CPET) were analyzed for fasting triglyceride (TG, mg/dL) and fasting blood glucose (FBG, mg/dL). CPET was performed following a standardized symptom-limited incremental protocol. Before every CPET, static lung function testing is performed on patients. This testing is calibrated using reference gases for accuracy. Dynamic lung function is then evaluated using SCHILLER bicycle ergometers (Baar, Switzerland) [ 13 ] .Following a short rest, participants performed 2–3 minutes of unloaded cycling at 60 revolutions per minute. Subsequently, the workload was progressively increased. Increments of 10% of each participant's estimated exercise power (calculated from age, height, and weight) were added until oxygen uptake and carbon dioxide excretion achieved equilibrium [ 14 ].The exercise was continued until the criteria for test termination, as set by the American Heart Association [ 15 ] , were met. Meanwhile, rehabilitation technicians recorded various CPET test outcomes, including peak oxygen uptake, anaerobic threshold oxygen uptake and other physiological metrics. Model Specification Three statistical models were constructed for this study. Model 1 was unadjusted. Model 2 was adjusted for age and sex. Given that age and sex are fundamental, unmodifiable cardiovascular risk variables, they were routinely adjusterd as covariates in studies assessing the prognostic value of the TyG index [ 16 – 18 ] . Furthermore, given the natural decline in cardiorespiratory fitness with age and the generally superior core CPET metrics in males compared to females, adjustment for age and sex is an essential step to minimize relevant confounding bias [ 19 , 20 ] In addition to age and sex, model 3 was adjusted for smoking status, drinking status, hypertension, and diabetes. These variables were included to control for potential confounding from traditional cardiovascular risk factors. Similar adjustment protocols have been employed in prior research [ 21 ] . Index Calculation The TyG index was calculated using the formula: TyG = ln [fasting triglycerides (TG, mg/dL) × glucose (mg/dL)/2] The modified TyG indices were calculated as follows: peak O₂ pulse_ TyG = peak O₂ pulse / TyG index peak VO₂/kg_ TyG = peak VO₂/kg / TyG index AT VO₂/kg_ TyG = AT VO₂/kg / TyG index The TyG index and the modified TyG indices were grouped into tertiles as follows: TyG (index), tertile 1: 9.04 peak VO₂/kg_ TyG (mL/kg/min), tertile 1: 1.95 peak O₂ pulse_ TyG (mL/beat), tertile 1: 1.20 AT VO₂/kg_ TyG (mL/kg/min), tertile 1: 1.34 Statistical Analysis Continuous variables are presented as mean ± standard deviation (SD), and categorical variables as number (percentage). Baseline characteristics were summarized according to tertiles of the TyG index. Spearman’s rank correlation coefficients were used to evaluate associations between the TyG index and its modified indices (peak O₂ pulse_ TyG, peak VO₂/kg_ TyG, AT VO₂/kg_ TyG). The cumulative incidence of events across tertiles of the TyG index and its modified indices was illustrated using Kaplan-Meier curves. Multivariable-adjusted Cox proportional hazards regression models were employed to assess the associations of the TyG index and its modified indices with cardiovascular outcomes, generating hazard ratios (HRs) and 95% confidence intervals (CIs). The proportional hazards assumption was validated using Schoenfeld residual tests, with no significant violations identified. Restricted cubic spline models (with knots at the 10th, 50th, and 90th percentiles) were applied to explore dose-response relationships, using the median value of each index as the reference. The predictive performance of the TyG index and its modified indices for MACE was evaluated using time-dependent Harrell’s C-index and AUC values. We performed mediation analysis to examine both direct and indirect associations between the TyG index and MACE through CPET parameters. In this analytical framework, elevated TyG index values served as the predictor variable (X), reduced CPET parameters functioned as the mediator variable (M), and MACE occurrence represented the outcome variable (Y). Additionally, the study conducted a complementary mediation analysis to evaluate whether CPET parameters mediated the relationship between TyG index and MACE events. All statistical analyses were conducted using R version 4.4.3. A two-sided p-value < 0.05 was considered statistically significant. Results Characteristics of the study population A total of 8,803 participants (6,771 male and 2,032 female) were included in this study. The mean age was 57.39 ± 8.97 years. Significant differences were found in age, smoking status, drinking status, hypertension, diabetes, BMI, FPG, hsCRP, TG, HDL, LVEF, WBC, RBC, Hb, Scr, SUA, and Plt. Other variables showed no statistically significant differences (Table 1 ). TyG was not strongly correlated with the modified TyG indices, and the Spearman’s coefficients for peak VO 2 /kg_ TyG, peak O 2 pulse_ TyG, and AT VO 2 /kg_ TyG were 0.18, 0.31, and 0.25, respectively (Fig. 1 ). Table 1 Characteristics of 8, 803 participants according to baseline TyG levels Overall Baseline TyG index P value Tertile 1 Tertile 2 Tertile 3 Participants, No. 8, 803 2, 935 2, 934 2, 934 Age, years, mean (SD) 57.39 ± 8.97 58.43 ± 8.77 57.53 ± 8.81 56.21 ± 9.17 < 0.001 * Sex, Male, n (%) 6, 771 (76.92%) 2, 274 (77.5%) 2, 235 (76.2%) 2, 262 (77.1%) 0.477 Smoking status, n (%) Never 4, 201 (47.72%) 1, 467 (50%) 1, 432 (48.8%) 1, 302 (44.4%) < 0.001 * Former 1, 451 (16.48%) 515 (17.5%) 460 (15.7%) 476 (16.2%) Current 3, 151 (35.79%) 953 (32.5%) 1, 042 (35.5%) 1, 156 (39.4%) Drinking status, n (%) Never 6, 566 (74.59%) 2, 183 (74.4%) 2, 203 (75.1%) 2, 180 (74.3%) 0.029 * Former 588 (6.68%) 229 (7.8%) 178 (6.1%) 181 (6.2%) Current 1, 649 (18.73%) 523 (17.8%) 553 (18.8%) 573 (19.5%) Hypertension, n (%) 5, 028 (57.12%) 1, 545 (52.6%) 1, 713 (58.4%) 1, 770 (60.3%) < 0.001 * Diabetes, n (%) 2, 283 (25.93%) 429 (14.6%) 607 (20.7%) 1, 247 (42.5%) < 0.001 * BMI, kg/m 2 < 0.001 * Continuous 26.37 ± 25.28 25.32 ± 14.17 26.85 ± 37.69 26.95 ± 17.14 28 1, 816 (20.63%) 443 (15.1%) 603 (20.6%) 770 (26.2%) FPG, mg/dL, mean (SD) 116.63 ± 44.45 94.98 ± 16.39 108.51 ± 27.62 146.39 ± 58.96 < 0.001 * hsCRP, mg/L, median (IQR) 1.20 (0.50–3.10) 0.80 (0.40–2.40) 1.2 (0.50–3.10) 1.60 (0.70–3.80) < 0.001 * TG, mg/dL, median (IQR) 119.54 (86.33-169.13) 77.9 (64.6–91.2) 125.0 (107.0-146.0) 196.0 (155.0-254.0) < 0.001 * HDL, mg/dL, mean (SD) 41.43 ± 9.40 43.07 ± 9.72 40.86 ± 8.59 40.37 ± 9.63 < 0.001 * CK, U/L median (IQR) 89 (65–118) 90 (66–118) 89 (65–118) 89 (64–119) 0.175 LEVF, %, mean (SD) 61.36 ± 5.97 61.63 ± 5.87 61.27 ± 6.03 61.17 ± 5.99 0.009 * WBC, ×10⁹/L, mean (SD) 8.03 ± 2.68 7.68 ± 2.65 8.12 ± 2.70 8.28 ± 2.67 < 0.001 * RBC, ×10¹²/L, mean (SD) 4.53 ± 0.46 4.47 ± 0.44 4.54 ± 0.45 4.59 ± 0.47 < 0.001 * Hb, mean, g/L (SD) 139.52 ± 13.64 138.27 ± 13.33 139.68 ± 13.48 140.61 ± 14.00 < 0.001 * SCr, µmol/L, mean (SD) 70.25 ± 21.30 69.52 ± 15.07 70.77 ± 28.11 70.45 ± 18.53 0.066 SUA, µmol/L, mean (SD) 350.32 ± 88.42 335.61 ± 80.59 353.60 ± 88.63 361.74 ± 93.57 < 0.001 * PLT, ×10⁹/L, mean (SD) 221.95 ± 54.51 215.10 ± 54.54 224.99 ± 53.33 225.76 ± 55.02 < 0.001 * Abbreviations: Data are presented as mean ± SD for normally distributed continuous variables, median (IQR) for skewed continuous variables, and n (%) for categorical variables. Statistical analysis: Differences among groups compared using One-way ANOVA for normally distributed variables. Kruskal-Wallis H test used for skewed variables. Chi-square test used for categorical variables. BMI, body mass index, calculated as weight in kilograms divided by height in meters squared; CK, creatine kinase; FPG, fasting plasma glucose; Hb, hemoglobin; HDL, high-density lipoprotein; hsCRP, high-sensitivity C-reactive protein; LVEF, left ventricular ejection fraction; PLT, platelet; RBC, red blood cell; SCr, serum creatinine; SUA, serum uric acid; TG, triglyceride; TyG, triglyceride-glucose index; WBC, white blood cell. * Indicates a statistically significant difference among the three groups (P < 0.05). Associations of TyG and modified TyG indices with MACE. Figure 2 presented a positive dose-response relationship between the TyG index and MACE risk, and an inverse dose-response relationship between the modified TyG indices and MACE risk. In the model 2 (age and sex were adjusted), participants in the highest tertile (tertile 3) of the TyG index had a significantly increased risk of MACE (HR 1.145, 95% CI 1.012–1.294, P = 0.031). In contrast, the highest tertiles (tertile 3) of the modified TyG indices were associated with a dose-dependent reduction in MACE risk compared to the lowest tertile (tertile 1): peak VO₂/ kg_ TyG: HR 0.828, 95% CI 0.726–0.944, P = 0.005; peak O₂ pulse_ TyG: HR 0.831, 95% CI 0.724–0.953, P = 0.008; AT VO₂/ kg_ TyG: HR 0.878, 95% CI 0.774–0.995, P = 0.042. In the model 3 (age, sex, smoking status, drinking status, hypertension and diabetes were adjusted), compared with tertile 1, the adjusted HRs (95% CIs) for MACE in tertile 3 were: TyG: 1.169 (1.027–1.330), P = 0.018; peak VO₂/ kg_ TyG: 0.814 (0.711–0.932), P = 0.003; peak O₂ pulse_ TyG: 0.835 (0.727–0.959), P = 0.011; AT VO₂/ kg_ TyG: 0.870 (0.766–0.990), P = 0.034. (Table 2 ) Table 2 Associations of TyG index and modified TyG index with MACE onset index tertile 1 tertile 2 tertile 3 HR 95%CI P value HR 95%CI P value TyG Model 1 Ref 1.147 1.011–1.301 0.033 * 1.155 1.022–1.305 0.021 # Model 2 1.150 1.013–1.304 0.030 * 1.145 1.012–1.294 0.031 # Model 3 1.162 1.024–1.319 0.020 * 1.169 1.027–1.330 0.018 # peak VO 2 /kg_ TyG Ref Model 1 0.868 0.772–0.976 0.018 * 0.841 0.741–0.955 0.007 # Model 2 0.865 0.768–0.974 0.016 * 0.828 0.726–0.944 0.005 # Model 3 0.855 0.759–0.964 0.011 * 0.814 0.711–0.932 0.003 # peak O 2 pulse_ TyG Ref Model 1 0.943 0.836–1.063 0.337 0.854 0.755–0.967 0.013 # Model 2 0.932 0.819–1.060 0.283 0.831 0.724–0.953 0.008 # Model 3 0.935 0.822–1.064 0.311 0.835 0.727–0.959 0.011 # AT VO 2 /kg_ TyG Ref Model 1 0.972 0.864–1.094 0.641 0.872 0.771–0.987 0.031 # Model 2 0.977 0.867-1.100 0.700 0.878 0.774–0.995 0.042 # Model 3 0.976 0.866–1.101 0.696 0.870 0.766–0.990 0.034 # Abbreviations: HR, hazard ratio; CI, confidence interval; TyG, triglyceride-glucose index; Peak VO 2 /kg, peak volume of oxygen per kilogram; Peak O 2 pulse, peak oxygen pulse; AT VO 2 /kg, anaerobic threshold volume of oxygen per kilogram. * Indicates a statistically significant difference between tertile 1 and tertile 2 (P < 0.05). # Indicates a statistically significant difference between tertile 1 and tertile 3(P < 0.05). Model 1: unadjusted; model 2: age and sex were adjusted; Model 3: age, sex, hypertension, diabetes, smoking status and drinking status were adjusted. Model 2 was adjusted for age and sex. Given that age and sex are fundamental, unmodifiable cardiovascular risk variables, they were routinely adjusterd as covariates in studies assessing the prognostic value of the TyG index [ 1 – 3 ] . Furthermore, given the natural decline in cardiorespiratory fitness with age and the generally superior core CPET metrics in males compared to females, adjustment for age and sex is an essential step to minimize relevant confounding bias [ 4 , 5 ] In addition to age and sex, model 3 was adjusted for smoking status, drinking status, hypertension, and diabetes. These variables were included to control for potential confounding from traditional cardiovascular risk factors. Similar adjustment protocols have been employed in prior research [ 6 ] . Predictive capacity comparison The study calculated time-dependent Harrell’s C-indices for four TyG-related parameters: TyG, peak VO 2 /kg_ TyG, peak O 2 pulse_ TyG and AT VO 2 /kg_ TyG. These indices showed significant predictive value for MACE (Fig. 3 ). The C-index values (95% CI) were: 0.565 (0.563–0.568) for TyG; 0.579 (0.577–0.581) for peak VO 2 /kg_ TyG; 0.565 (0.563–0.567) for peak O 2 pulse_ TyG; and 0.569 (0.568–0.571) for AT VO 2 /kg_ TyG. The time-dependent AUC indicated that the TyG index and its modified indices (including peak VO₂/kg_ TyG, peak O₂ pulse_ TyG, and AT VO₂/kg_ TyG) consistently exhibited significant predictive value for MACE throughout the follow-up period (Fig. 4 ). Stratified Analysis In Scenario 1, participants were stratified by median peak VO₂/kg. Among those with above-median peak VO₂/kg, a higher TyG index (above median) was associated with an increased risk of MACE (HR: 1.199, 95% CI: 1.027-1.400, P = 0.022). Similarly, in the group with below-median peak VO₂/kg, a higher TyG index (above median) also significantly increased the risk of MACE (HR: 1.162, 95% CI: 1.007–1.341, P = 0.040). (Table 3 ) Table 3 Risk reclassification of MACE based on the TyG index and peakVO 2 kg HR 95%CI P value Scenario 1 peak VO 2 /kg >median TyGmedian 1.199 1.027-1.400 0.022 * peak VO 2 /kg <median TyGmedian 1.162 1.007–1.341 0.040 * Scenario 2 TyGmedian - - peak VO 2 /kg median peak VO 2 /kg >median - - peak VO 2 /kg <median 1.142 0.984–1.327 0.081 Abbreviations: Hazard ratios (HR) and 95% confidence intervals (CI) were derived from Cox proportional hazards models. Median values were used as cut-off points for both the TyG index and peak VO2/kg <median. * Indicates a statistically significant difference (P < 0.05) compared to the TyG < median reference group within the same aerobic capacity stratum. # Indicates a statistically significant difference (P < 0.05) compared to the peak VO 2 /kg <median reference group within the same TyG index stratum. TyG, triglyceride-glucose index; peak VO 2 /kg, peak oxygen uptake per kilogram; MACE, major adverse cardiovascular events; HR, hazard ratio; CI, confidence interval. Scenario 1: Association between TyG and MACE across peak VO 2 /kg groups; Scenario 2: association between peak VO 2 /kg and MACE across TyG groups In Scenario 2, stratification was performed by the TyG index. Participants with a below-median TyG index and low peak VO₂/kg (below median) had a significantly higher risk of MACE (HR: 1.186, 95% CI: 1.016–1.384, P = 0.030). However, among those with an above-median TyG index, low peak VO₂/kg was not significantly associated with an increased risk of MACE (HR: 1.142, 95% CI: 0.984–1.327, P = 0.081). (Table 3 ) Mediation analysis In the model 3 (age, sex, hypertension, diabetes, smoking status and drinking status were adjusted) with the TyG index as the independent variable, the average causal mediation effect (ACME) was 0.00376 (95% CI 0.00217-0.0100; P < 0.001). The average direct effect (ADE) was 0.01726 (P = 0.008). Consequently, the proportion of the total effect mediated was 17.90% (P < 0.001). When peak VO₂/kg was included as the independent variable, the ACME was − 0.0000737 (95% CI -0.000286-0.0000; P = 0.520), while the ADE was − 0.0048 (P < 0.001). Thus, the mediated proportion was 1.51% (P = 0.520). (Fig. 5 ) Discussion The present study innovatively integrated the TyG index with key CPET parameters to develop modified TyG indices. The study subsequently evaluated and compared the predictive value of the traditional TyG index versus these novel indices for MACE. This study analysis demonstrated that although the TyG index and the modified indices showed only a weak-to-moderate correlation, both were independent predictors of MACE risk. Notably, for MACE risk stratification, the peak VO₂/kg_TyG and the AT VO₂/kg_TyG presented significantly superior predictive accuracy compared to the traditional TyG index, with the peak VO₂/kg_TyG index performing the best. Moreover, mediation analysis revealed that peak VO₂/kg partially mediated the association between the TyG index and MACE. The TyG index has been extensively validated as an independent predictor of cardiovascular outcomes. For instance, a large retrospective cohort study by Lee et al. confirmed that elevated TyG levels independently predict all-cause and cardiovascular mortality[8]. This is further supported by meta-analytic evidence, which synthesizes data from multiple studies to confirm significant associations between the TyG index and the risks of cardiovascular events, all-cause mortality, and cardiovascular-specific mortality [ 22 ] . Moreover, longitudinal changes in the TyG index (including its cumulative burden, fluctuation, and rising trend) have also been linked to a higher incidence of cardiovascular events [ 23 – 26 ] . In this regard, our findings regarding the predictive value of the traditional TyG index align with the established body of evidence. CPET has emerged in recent years as the gold-standard, non-invasive method for comprehensively assessing cardiorespiratory fitness, gas exchange efficiency, and exercise tolerance, providing established benchmarks for evaluating cardiovascular functional reserve [ 27 , 28 ] . Consequently, the present study innovatively integrated the TyG index, reflecting basal metabolic state, with key CPET parameters representing dynamic cardiorespiratory response to construct a novel modified TyG indices. Peak VO₂/kg, peak O₂ pulse, and AT VO₂/kg serve as key indicators of cardiorespiratory fitness [ 29 ] . These parameters directly quantify the overall efficiency of oxygen transport and utilization dynamics under exercise stress[29]. Although physiological interplay exists between cardiorespiratory function and the glucolipid metabolic dysregulation reflected by the TyG index, these systems occupy distinct physiological domains: the former governs the delivery and utilization of energetic substrates, while the latter primarily regulates their metabolism. Consistent with this conceptual distinction, the study presented only a weak correlation between the TyG index and the modified TyG indices. This key finding indicates that the modified TyG indices do not merely amplify or replicate the metabolic information of the original index. Instead, they structurally incorporate a novel dimension of cardiorespiratory reserve, thereby creating a more comprehensive and integrative risk assessment tool. The present study confirmed that both the TyG index and the modified TyG indices were independent predictors of the long-term risk of MACE. Notably, with regard to predictive performance, both peak VO₂/kg_ TyG and AT VO₂/kg_ TyG significantly outperformed the TyG index, with peak VO₂/kg_ TyG presented the most prominent superiority. The underlying mechanism for this enhanced predictive capacity can be primarily attributed to the rich pathophysiological information captured by peak VO₂/kg. Specifically, peak VO₂/kg is a comprehensive indicator reflecting overall cardiopulmonary function, not only assessing a patient's maximum cardiac pumping capacity and functional reserve but also providing an integrated assessment of cardiovascular disease severity [ 30 – 32 ] . Moreover, robust clinical evidence confirms a clear association between peak VO₂/kg levels and cardiovascular mortality, where an improvement in peak VO₂/kg is indicative of enhanced cardiac function and is associated with a corresponding reduction in cardiovascular mortality [ 33 – 37 ] . Precisely because of the high clinical predictive value inherent in peak VO₂/kg itself and its reflection of the integrated health status of the cardiovascular system, its incorporation into the TyG index significantly augmented the model’s predictive capacity for MACE risk. In addition, the mediation analysis reveals distinct mechanistic pathways for the TyG index and peak VO₂/kg. At the level of cardiac energy metabolism, while the myocardium preferentially utilizes fatty acids for energy production, it possesses the inherent capacity to adapt by switching ATP generation pathways in response to substrate availability to maintain energy homeostasis [ 38 – 40 ] . However, insulin resistance disrupts this metabolic flexibility. It forces the heart into an excessive reliance on fatty acid oxidation, particularly impairing the crucial shift from fatty acid to glucose utilization that should occur under stress conditions, such as myocardial injury[41]. This metabolic inflexibility results in increased myocardial lipid uptake and accumulation, inducing lipotoxicity [ 41 ] . CPET can provide a critical reference for this assessment. It precisely quantifies the threshold for myocardial metabolic transition by effectively assessing the balance between myocardial metabolic demand and supply during exercise. Therefore, CPET combined with an evaluation of insulin resistance status, provides a more accurate assessment of cardiac metabolic reserve capacity. This integrated approach offers a powerful tool for the diagnosis and risk stratification of metabolic heart disease. The present study provides a novel method for predicting the risk of MACE in ACS patients following PCI. Nevertheless, the predictive performance and the underlying mechanisms of this model require validation in prospective cohort studies and fundamental research. Limitations Several limitations of this study should be acknowledged. First, the retrospective design inherently carries risks of selection and information bias, despite the application of rigorous inclusion/exclusion criteria and thorough data cleaning. Second, the absence of prospective controls and interventions precludes establishing a causal relationship between the TyG index and MACE. Third, although adjustments were made for multiple known cardiovascular risk factors, the influence of unmeasured confounders such as inflammatory states, medication adherence, and lifestyle factors cannot be entirely excluded. Another limitation lies in the dependence of CPET parameters on patient cooperation and the consistency of test administration. Despite all tests being conducted by experienced technicians under standardized protocols, CPET results may still be affected by subjective effort and exercise tolerance. First, the retrospective design inherently carries risks of selection and information bias, despite the application of rigorous inclusion/exclusion criteria and thorough data cleaning. Conclusions The TyG index and the modified TyG indices presented significantly independent associations with MACE in the study population. Peak VO₂/kg_ TyG presented significantly higher predictive accuracy for MACE risk stratification compared with the TyG index. In addition, peak VO₂/kg significantly mediates the association between the TyG index and MACE. Declarations Competing interests The authors declare no competing interests. Ethics declarations The Ethics Committee of General Hospital of Northern Theater Command approved the research protocol [Y (2025)444]. Consent to participate Informed consent was obtained from all participants or their legally authorized representative. Funding This research was funded by the Liaoning Province Science & Technology Project (2025JH2/101800045). The funder had no role in the design of the study, collection and analysis of the data, and writing of the paper. Author Contribution Yaling Han conceived the study concept. Yaling Han and Quanyu Zhang designed the trial. Yanchun Liang and Jian Zhang provided critical appraisal of the study. Yan-Xia Wang, Cai-Lian Wang, Yi Zhang, Lin Pang and Sheng-Yi Wang oversaw the cardiac rehabilitation program. SiCheng Ning and Ning Sun conducted the research and were responsible for data collection and curation. SiCheng Ning and Muxing Bo performed the statistical analysis, while Muxing Bo handled validation and visualization. SiCheng Ning drafted the manuscript, and Quanyu Zhang critically revised it. All authors approved the final version and agree to be accountable for all aspects of the work, ensuring its integrity and accuracy. Acknowledgments We thank the patients and family for their participation. Data availability The datasets generated and analyzed during the current study are not publicly available due to confidentiality restrictions but can be made available by the corresponding author upon reasonable request. References Wu Z, Zhou D, Liu Y, et al. Association of TyG index and TG/HDL-C ratio with arterial stiffness progression in a non-normotensive population. Cardiovasc Diabetol. 2021;20(1):134. 10.1186/s12933-021-01330-6 . Louie JZ, Shiffman D, McPhaul MJ, Melander O. 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Cardiovasc Diabetol. 2022;21(1):141. 10.1186/s12933-022-01577-7 . Zhan Y, Li L, Zhou J, et al. Efficacy of vericiguat in patients with chronic heart failure and reduced ejection fraction: a prospective observational study. BMC Cardiovasc Disord. 2025;25(1):83. 10.1186/s12872-025-04477-2 . Guazzi M, Wilhelm M, Halle M, et al. Exercise testing in heart failure with preserved ejection fraction: an appraisal through diagnosis, pathophysiology and therapy - A clinical consensus statement of the Heart Failure Association and European Association of Preventive Cardiology of the European Society of Cardiology. Eur J Heart Fail. 2022;24(8):1327–45. 10.1002/ejhf.2601 . Arena R, Sietsema KE. Cardiopulmonary exercise testing in the clinical evaluation of patients with heart and lung disease. Circulation. 2011;123(6):668–80. 10.1161/CIRCULATIONAHA.109.914788 . Wang C, Xing J, Zhao B, et al. The Effects of High-Intensity Interval Training on Exercise Capacity and Prognosis in Heart Failure and Coronary Artery Disease: A Systematic Review and Meta-Analysis. Cardiovasc Ther. 2022;2022:4273809. 10.1155/2022/4273809 . Carbone S, Kim Y, Kachur S, et al. Peak oxygen consumption achieved at the end of cardiac rehabilitation predicts long-term survival in patients with coronary heart disease. Eur Heart J Qual Care Clin Outcomes. 2022;8(3):361–7. 10.1093/ehjqcco/qcab032 . Belardinelli R, Lacalaprice F, Carle F, et al. Exercise-induced myocardial ischaemia detected by cardiopulmonary exercise testing. Eur Heart J. 2003;24(14):1304–13. 10.1016/s0195-668x(03)00210-0 . Barmeyer A, Meinertz T. Anaerobic threshold and maximal oxygen uptake in patients with coronary artery disease and stable angina before and after percutaneous transluminal coronary angioplasty. Cardiology. 2002;98(3):127–31. 10.1159/000066320 . Pack QR, Goel K, Lahr BD, et al. Participation in cardiac rehabilitation and survival after coronary artery bypass graft surgery: a community-based study. Circulation. 2013;128(6):590–7. 10.1161/CIRCULATIONAHA.112.001365 . Mitchell BL, Lock MJ, Davison K, Parfitt G, Buckley JP, Eston RG. What is the effect of aerobic exercise intensity on cardiorespiratory fitness in those undergoing cardiac rehabilitation? A systematic review with meta-analysis. Br J Sports Med. 2019;53(21):1341–51. 10.1136/bjsports-2018-099153 . Swank AM, Horton J, Fleg JL, et al. Modest increase in peak VO2 is related to better clinical outcomes in chronic heart failure patients: results from heart failure and a controlled trial to investigate outcomes of exercise training. Circ Heart Fail. 2012;5(5):579–85. 10.1161/CIRCHEARTFAILURE.111.965186 . Myers J, Gullestad L, Vagelos R, et al. Cardiopulmonary exercise testing and prognosis in severe heart failure: 14 mL/kg/min revisited. Am Heart J. 2000;139(1 Pt 1):78–84. 10.1016/s0002-8703(00)90312-0 . Chanda D, Luiken JJFP, Glatz JFC. Signaling pathways involved in cardiac energy metabolism. FEBS Lett. 2016;590(15):2364–74. 10.1002/1873-3468.12297 . Goodwin GW, Taylor CS, Taegtmeyer H. Regulation of energy metabolism of the heart during acute increase in heart work. J Biol Chem. 1998;273(45):29530–9. 10.1074/jbc.273.45.29530 . van der Vusse GJ, van Bilsen M, Glatz JF. Cardiac fatty acid uptake and transport in health and disease. Cardiovasc Res. 2000;45(2):279–93. 10.1016/s0008-6363(99)00263-1 . Zhou YT, Grayburn P, Karim A, et al. Lipotoxic heart disease in obese rats: implications for human obesity. Proc Natl Acad Sci U S A. 2000;97(4):1784–9. 10.1073/pnas.97.4.1784 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8868777","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609458366,"identity":"9348bbb3-d296-4db9-a67d-bc2e595b875e","order_by":0,"name":"Sicheng Ning","email":"","orcid":"","institution":"General Hospitial of Northern Theater Command","correspondingAuthor":false,"prefix":"","firstName":"Sicheng","middleName":"","lastName":"Ning","suffix":""},{"id":609458367,"identity":"0c9b7579-3a66-4215-bc7a-19c510211477","order_by":1,"name":"Ning Sun","email":"","orcid":"","institution":"General Hospitial of Northern Theater 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Theater Command","correspondingAuthor":true,"prefix":"","firstName":"QuanYu","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-02-13 07:38:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8868777/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8868777/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105195863,"identity":"3301f0fc-41be-4668-a1f7-d3fa32b369cd","added_by":"auto","created_at":"2026-03-23 10:17:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":124174,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman’s correlation of TyG and modified indices.\u003c/p\u003e\n\u003cp\u003eAbbreviations: TyG, triglyceride-glucose index; peak VO\u003csub\u003e2\u003c/sub\u003e/kg, peak volume of oxygen per kilogram; peak O\u003csub\u003e2\u003c/sub\u003e pulse, peak oxygen pulse; AT VO\u003csub\u003e2\u003c/sub\u003e/kg, anaerobic threshold volume of oxygen per kilogram.\u003c/p\u003e\n\u003cp\u003eColor intensity represents the strength and direction of the Spearman correlation coefficients. The strength of the correlation between two variables is represented by the color at the intersection.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8868777/v1/da26932c842106fdf1ca2724.jpg"},{"id":105195859,"identity":"a58a78c9-83c1-4620-86f3-641fe823619b","added_by":"auto","created_at":"2026-03-23 10:17:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":388074,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response association of the TyG index and the modified indices with the risk of MACE\u003c/p\u003e\n\u003cp\u003eA: positive dose-response relationship between the TyG index and MACE risk. B: inverse dose-response relationship between the peak VO\u003csub\u003e2\u003c/sub\u003e/kg_ TyG and MACE risk. C: inverse dose-response relationship between AT VO\u003csub\u003e2\u003c/sub\u003e/kg_ TyG and MACE risk. D: inverse dose-response relationship between AT VO\u003csub\u003e2\u003c/sub\u003e/kg_ TyG and MACE risk.\u003c/p\u003e\n\u003cp\u003eTyG, triglyceride-glucose index; peak VO\u003csub\u003e2\u003c/sub\u003e/kg, peak volume of oxygen per kilogram; peak O\u003csub\u003e2\u003c/sub\u003e pulse, peak oxygen pulse; AT VO\u003csub\u003e2\u003c/sub\u003e/kg, anaerobic threshold volume of oxygen per kilogram.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8868777/v1/8550503d197852decd2c375a.jpg"},{"id":105195861,"identity":"2eb5c828-9684-418e-a7b1-a9abdd749bab","added_by":"auto","created_at":"2026-03-23 10:17:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":196100,"visible":true,"origin":"","legend":"\u003cp\u003eC-index values of TyG and modified indices for MACE\u003c/p\u003e\n\u003cp\u003eAbbreviations: TyG, triglyceride-glucose index; peak VO\u003csub\u003e2\u003c/sub\u003e/kg, peak volume of oxygen per kilogram; peak O\u003csub\u003e2\u003c/sub\u003e pulse, peak oxygen pulse; AT VO\u003csub\u003e2\u003c/sub\u003e/kg, anaerobic threshold volume of oxygen per kilogram; C-index, concordance index.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8868777/v1/b74aed7841c3190731eb71da.jpg"},{"id":105195862,"identity":"48f531f9-5643-471c-87db-38578ca0280c","added_by":"auto","created_at":"2026-03-23 10:17:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":197919,"visible":true,"origin":"","legend":"\u003cp\u003eAUCs value of TyG and modified indices for MACE\u003c/p\u003e\n\u003cp\u003eTyG, triglyceride-glucose index; peak VO\u003csub\u003e2\u003c/sub\u003e/kg, peak volume of oxygen per kilogram; peak O\u003csub\u003e2\u003c/sub\u003e pulse, peak oxygen pulse; AT VO\u003csub\u003e2\u003c/sub\u003e/kg, anaerobic threshold volume of oxygen per kilogram; AUC, area under curve.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8868777/v1/66f5f35792962f0b868964fb.jpg"},{"id":105564012,"identity":"7fc42190-283e-41fe-afc9-443dcd600f26","added_by":"auto","created_at":"2026-03-27 12:48:27","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":351477,"visible":true,"origin":"","legend":"\u003cp\u003eReciprocal mediation between the TyG index and peak VO₂/kg on the risk of MACE\u003c/p\u003e\n\u003cp\u003eAbbreviations: TyG: triglyceride-glucose index; peak VO₂/kg: peak volume of oxygen per kilogram, MACE, major adverse cardiovascular events.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8868777/v1/d35bd120f9e9df1fe61cb52d.jpg"},{"id":105569416,"identity":"ef858180-f332-4806-884e-1bdbe82d7ad2","added_by":"auto","created_at":"2026-03-27 13:12:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2265424,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8868777/v1/4266a66a-fc13-48d0-8587-d51b287d8530.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of Triglyceride-Glucose (TyG) Index and the Modified TyG Index combined with Cardiopulmonary Exercise Testing (CPET) Parameters for Major Adverse Cardiovascular Events (MACE)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInsulin resistance, one of important pathophysiological mechanism underlying the development and progression of atherosclerotic cardiovascular disease (ASCVD), demonstrates significant clinical value in cardiovascular risk stratification \u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. While the hyperinsulinemic-euglycemic clamp (HIEC) remains the gold standard for assessing IR\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, its clinical utility is constrained by procedural complexity and high costs. In recent years, the triglyceride-glucose (TyG) index has emerged as a reliable alternative marker for IR in large-scale epidemiological studies, owing to its accessibility and reproducibility\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Previous research shown the TyG index correlates with subclinical atherosclerosis markers such as carotid intima-media thickness (CIMT) and coronary artery calcium score (CACS) and independently predicts cardiovascular risks such as acute coronary syndrome\u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. While the TyG index holded significant value as a static biomarker of insulin resistance, its characteristic of being dependent on serum biochemical indicators limits its ability to assess individual functional reserve\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCardiopulmonary exercise testing (CPET) is an objective method for quantifying cardiopulmonary function. By monitoring key physiological responses such as gas exchange, electrocardiogram (ECG), and hemodynamics during exercise, it aids cardiovascular disease management through diagnosis, prognosis assessment, and tracking treatment effectiveness. Furthermore, it accurately identifies exercise-induced myocardial ischemia, malignant arrhythmias, and abnormal hemodynamic responses, emphasizing its clinical significance in cardiovascular medicine\u003csup\u003e[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe purpose of this present study is to develop an optimized TyG index through integration of CPET parameters. The study aimed to compare the predictive performance of the conventional versus modified TyG indices for major adverse cardiovascular events (MACE). These findings may provide novel evidence for refining cardiovascular risk stratification models.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design and Population\u003c/p\u003e \u003cp\u003eThis retrospective cohort study enrolled patients who underwent CPET at the General Hospital of Northern Theater Command from March, 2016 to May, 2019. Inclusion criteria were: Patients diagnosed with ACS who successfully underwent PCI. Clinically stable and able to tolerate and complete a standard CPET. Exclusion criteria comprised: Presence of co-existing conditions such as hereditary cardiomyopathy, severe valvular heart disease, end-stage renal disease, or active malignancy. Acute or active medical conditions that could interfere with cardiopulmonary function assessment, including (but not limited to) uncontrolled severe infection within the past 4 weeks, acute pulmonary embolism, or a stroke within the preceding 3 months. The study protocol was approved by the Ethics Committee of General Hospital of Northern Theater Command [Y (2025)444]. A waiver of informed consent was granted due to the retrospective nature of the study and anonymized data processing. This study adhered to the principles of the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eData Collection and Definitions\u003c/p\u003e \u003cp\u003eBaseline clinical characteristics included age, sex, body mass index (BMI), smoking history, alcohol consumption, and comorbidities (e.g., hypertension, diabetes mellitus, prior acute myocardial infarction [AMI]). Hypertension was defined as systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or current use of antihypertensive therapy. Diabetes mellitus was diagnosed based on fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, 2-hour postprandial blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L, glycated hemoglobin (HbA1c)\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, or a confirmed diagnosis of diabetes with active treatment. Fasting venous blood samples (after \u0026ge;\u0026thinsp;8 hours of fasting) collected closest to the CPET date (typically within one week before or after CPET) were analyzed for fasting triglyceride (TG, mg/dL) and fasting blood glucose (FBG, mg/dL). CPET was performed following a standardized symptom-limited incremental protocol. Before every CPET, static lung function testing is performed on patients. This testing is calibrated using reference gases for accuracy. Dynamic lung function is then evaluated using SCHILLER bicycle ergometers (Baar, Switzerland)\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.Following a short rest, participants performed 2\u0026ndash;3 minutes of unloaded cycling at 60 revolutions per minute. Subsequently, the workload was progressively increased. Increments of 10% of each participant's estimated exercise power (calculated from age, height, and weight) were added until oxygen uptake and carbon dioxide excretion achieved equilibrium\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e].The exercise was continued until the criteria for test termination, as set by the American Heart Association\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, were met. Meanwhile, rehabilitation technicians recorded various CPET test outcomes, including peak oxygen uptake, anaerobic threshold oxygen uptake and other physiological metrics.\u003c/p\u003e \u003cp\u003eModel Specification\u003c/p\u003e \u003cp\u003eThree statistical models were constructed for this study. Model 1 was unadjusted. Model 2 was adjusted for age and sex. Given that age and sex are fundamental, unmodifiable cardiovascular risk variables, they were routinely adjusterd as covariates in studies assessing the prognostic value of the TyG index\u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Furthermore, given the natural decline in cardiorespiratory fitness with age and the generally superior core CPET metrics in males compared to females, adjustment for age and sex is an essential step to minimize relevant confounding bias\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e] In addition to age and sex, model 3 was adjusted for smoking status, drinking status, hypertension, and diabetes. These variables were included to control for potential confounding from traditional cardiovascular risk factors. Similar adjustment protocols have been employed in prior research\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIndex Calculation\u003c/p\u003e \u003cp\u003eThe TyG index was calculated using the formula:\u003c/p\u003e \u003cp\u003eTyG\u0026thinsp;=\u0026thinsp;ln [fasting triglycerides (TG, mg/dL) \u0026times; glucose (mg/dL)/2]\u003c/p\u003e \u003cp\u003eThe modified TyG indices were calculated as follows:\u003c/p\u003e \u003cp\u003epeak O₂ pulse_ TyG\u0026thinsp;=\u0026thinsp;peak O₂ pulse / TyG index\u003c/p\u003e \u003cp\u003epeak VO₂/kg_ TyG\u0026thinsp;=\u0026thinsp;peak VO₂/kg / TyG index\u003c/p\u003e \u003cp\u003eAT VO₂/kg_ TyG\u0026thinsp;=\u0026thinsp;AT VO₂/kg / TyG index\u003c/p\u003e \u003cp\u003eThe TyG index and the modified TyG indices were grouped into tertiles as follows:\u003c/p\u003e \u003cp\u003eTyG (index), tertile 1: \u0026lt;8.51, tertile 2: 8.51\u0026ndash;9.04, tertile 3: \u0026gt;9.04\u003c/p\u003e \u003cp\u003epeak VO₂/kg_ TyG (mL/kg/min), tertile 1: \u0026lt;1.54, tertile 2: 1.54\u0026ndash;1.95, tertile 3: \u0026gt;1.95\u003c/p\u003e \u003cp\u003epeak O₂ pulse_ TyG (mL/beat), tertile 1: \u0026lt;0.95, tertile 2: 0.95\u0026ndash;1.20, tertile 3: \u0026gt;1.20\u003c/p\u003e \u003cp\u003eAT VO₂/kg_ TyG (mL/kg/min), tertile 1: \u0026lt;1.12, tertile 2: 1.12\u0026ndash;1.34, tertile 3: \u0026gt;1.34\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and categorical variables as number (percentage). Baseline characteristics were summarized according to tertiles of the TyG index. Spearman\u0026rsquo;s rank correlation coefficients were used to evaluate associations between the TyG index and its modified indices (peak O₂ pulse_ TyG, peak VO₂/kg_ TyG, AT VO₂/kg_ TyG). The cumulative incidence of events across tertiles of the TyG index and its modified indices was illustrated using Kaplan-Meier curves. Multivariable-adjusted Cox proportional hazards regression models were employed to assess the associations of the TyG index and its modified indices with cardiovascular outcomes, generating hazard ratios (HRs) and 95% confidence intervals (CIs). The proportional hazards assumption was validated using Schoenfeld residual tests, with no significant violations identified. Restricted cubic spline models (with knots at the 10th, 50th, and 90th percentiles) were applied to explore dose-response relationships, using the median value of each index as the reference. The predictive performance of the TyG index and its modified indices for MACE was evaluated using time-dependent Harrell\u0026rsquo;s C-index and AUC values. We performed mediation analysis to examine both direct and indirect associations between the TyG index and MACE through CPET parameters. In this analytical framework, elevated TyG index values served as the predictor variable (X), reduced CPET parameters functioned as the mediator variable (M), and MACE occurrence represented the outcome variable (Y). Additionally, the study conducted a complementary mediation analysis to evaluate whether CPET parameters mediated the relationship between TyG index and MACE events. All statistical analyses were conducted using R version 4.4.3. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study population\u003c/h2\u003e \u003cp\u003eA total of 8,803 participants (6,771 male and 2,032 female) were included in this study. The mean age was 57.39\u0026thinsp;\u0026plusmn;\u0026thinsp;8.97 years. Significant differences were found in age, smoking status, drinking status, hypertension, diabetes, BMI, FPG, hsCRP, TG, HDL, LVEF, WBC, RBC, Hb, Scr, SUA, and Plt. Other variables showed no statistically significant differences (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). TyG was not strongly correlated with the modified TyG indices, and the Spearman\u0026rsquo;s coefficients for peak VO\u003csub\u003e2\u003c/sub\u003e/kg_ TyG, peak O\u003csub\u003e2\u003c/sub\u003e pulse_ TyG, and AT VO\u003csub\u003e2\u003c/sub\u003e /kg_ TyG were 0.18, 0.31, and 0.25, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of 8, 803 participants according to baseline TyG levels\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\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eBaseline TyG index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTertile 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTertile 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTertile 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipants, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8, 803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2, 934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2, 934\u003c/p\u003e \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\u003eAge, years, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.39\u0026thinsp;\u0026plusmn;\u0026thinsp;8.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.43\u0026thinsp;\u0026plusmn;\u0026thinsp;8.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.53\u0026thinsp;\u0026plusmn;\u0026thinsp;8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.21\u0026thinsp;\u0026plusmn;\u0026thinsp;9.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, Male, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6, 771 (76.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 274 (77.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2, 235 (76.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2, 262 (77.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\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\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4, 201 (47.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 467 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1, 432 (48.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 302 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1, 451 (16.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e515 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e460 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e476 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3, 151 (35.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e953 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1, 042 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 156 (39.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status, n (%)\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\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6, 566 (74.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 183 (74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2, 203 (75.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2, 180 (74.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e588 (6.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e229 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e178 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e181 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1, 649 (18.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e523 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e553 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e573 (19.5%)\u003c/p\u003e \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\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5, 028 (57.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 545 (52.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1, 713 (58.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 770 (60.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2, 283 (25.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e429 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e607 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 247 (42.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\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\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.37\u0026thinsp;\u0026plusmn;\u0026thinsp;25.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.32\u0026thinsp;\u0026plusmn;\u0026thinsp;14.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.85\u0026thinsp;\u0026plusmn;\u0026thinsp;37.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.95\u0026thinsp;\u0026plusmn;\u0026thinsp;17.14\u003c/p\u003e \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\u003e\u0026lt;\u0026thinsp;23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2, 816 (31.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 224 (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e870 (29.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e722 (24.6%)\u003c/p\u003e \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\u003e24-27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4, 171 (47.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1268 (43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1461 (49.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 442 (49.1%)\u003c/p\u003e \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\u003e\u0026gt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1, 816 (20.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e443 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e603 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e770 (26.2%)\u003c/p\u003e \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\u003eFPG, mg/dL, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.63\u0026thinsp;\u0026plusmn;\u0026thinsp;44.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.98\u0026thinsp;\u0026plusmn;\u0026thinsp;16.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108.51\u0026thinsp;\u0026plusmn;\u0026thinsp;27.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e146.39\u0026thinsp;\u0026plusmn;\u0026thinsp;58.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsCRP, mg/L, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20 (0.50\u0026ndash;3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80 (0.40\u0026ndash;2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2 (0.50\u0026ndash;3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60 (0.70\u0026ndash;3.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mg/dL, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.54 (86.33-169.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.9 (64.6\u0026ndash;91.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125.0 (107.0-146.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e196.0 (155.0-254.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL, mg/dL, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.43\u0026thinsp;\u0026plusmn;\u0026thinsp;9.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.07\u0026thinsp;\u0026plusmn;\u0026thinsp;9.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.86\u0026thinsp;\u0026plusmn;\u0026thinsp;8.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.37\u0026thinsp;\u0026plusmn;\u0026thinsp;9.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK, U/L median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (65\u0026ndash;118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (66\u0026ndash;118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (65\u0026ndash;118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89 (64\u0026ndash;119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEVF, %, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.36\u0026thinsp;\u0026plusmn;\u0026thinsp;5.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.63\u0026thinsp;\u0026plusmn;\u0026thinsp;5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.27\u0026thinsp;\u0026plusmn;\u0026thinsp;6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.17\u0026thinsp;\u0026plusmn;\u0026thinsp;5.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, \u0026times;10⁹/L, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC, \u0026times;10\u0026sup1;\u0026sup2;/L, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb, mean, g/L (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139.52\u0026thinsp;\u0026plusmn;\u0026thinsp;13.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.27\u0026thinsp;\u0026plusmn;\u0026thinsp;13.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139.68\u0026thinsp;\u0026plusmn;\u0026thinsp;13.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140.61\u0026thinsp;\u0026plusmn;\u0026thinsp;14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCr, \u0026micro;mol/L, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.25\u0026thinsp;\u0026plusmn;\u0026thinsp;21.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.52\u0026thinsp;\u0026plusmn;\u0026thinsp;15.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.77\u0026thinsp;\u0026plusmn;\u0026thinsp;28.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.45\u0026thinsp;\u0026plusmn;\u0026thinsp;18.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUA, \u0026micro;mol/L, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e350.32\u0026thinsp;\u0026plusmn;\u0026thinsp;88.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335.61\u0026thinsp;\u0026plusmn;\u0026thinsp;80.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e353.60\u0026thinsp;\u0026plusmn;\u0026thinsp;88.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e361.74\u0026thinsp;\u0026plusmn;\u0026thinsp;93.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT, \u0026times;10⁹/L, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221.95\u0026thinsp;\u0026plusmn;\u0026thinsp;54.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215.10\u0026thinsp;\u0026plusmn;\u0026thinsp;54.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e224.99\u0026thinsp;\u0026plusmn;\u0026thinsp;53.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e225.76\u0026thinsp;\u0026plusmn;\u0026thinsp;55.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for normally distributed continuous variables, median (IQR) for skewed continuous variables, and n (%) for categorical variables. Statistical analysis: Differences among groups compared using One-way ANOVA for normally distributed variables. Kruskal-Wallis H test used for skewed variables. Chi-square test used for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBMI, body mass index, calculated as weight in kilograms divided by height in meters squared; CK, creatine kinase; FPG, fasting plasma glucose; Hb, hemoglobin; HDL, high-density lipoprotein; hsCRP, high-sensitivity C-reactive protein; LVEF, left ventricular ejection fraction; PLT, platelet; RBC, red blood cell; SCr, serum creatinine; SUA, serum uric acid; TG, triglyceride; TyG, triglyceride-glucose index; WBC, white blood cell.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e* Indicates a statistically significant difference among the three groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociations of TyG and modified TyG indices with MACE.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presented a positive dose-response relationship between the TyG index and MACE risk, and an inverse dose-response relationship between the modified TyG indices and MACE risk. In the model 2 (age and sex were adjusted), participants in the highest tertile (tertile 3) of the TyG index had a significantly increased risk of MACE (HR 1.145, 95% CI 1.012\u0026ndash;1.294, P\u0026thinsp;=\u0026thinsp;0.031). In contrast, the highest tertiles (tertile 3) of the modified TyG indices were associated with a dose-dependent reduction in MACE risk compared to the lowest tertile (tertile 1): peak VO₂/ kg_ TyG: HR 0.828, 95% CI 0.726\u0026ndash;0.944, P\u0026thinsp;=\u0026thinsp;0.005; peak O₂ pulse_ TyG: HR 0.831, 95% CI 0.724\u0026ndash;0.953, P\u0026thinsp;=\u0026thinsp;0.008; AT VO₂/ kg_ TyG: HR 0.878, 95% CI 0.774\u0026ndash;0.995, P\u0026thinsp;=\u0026thinsp;0.042. In the model 3 (age, sex, smoking status, drinking status, hypertension and diabetes were adjusted), compared with tertile 1, the adjusted HRs (95% CIs) for MACE in tertile 3 were: TyG: 1.169 (1.027\u0026ndash;1.330), P\u0026thinsp;=\u0026thinsp;0.018; peak VO₂/ kg_ TyG: 0.814 (0.711\u0026ndash;0.932), P\u0026thinsp;=\u0026thinsp;0.003; peak O₂ pulse_ TyG: 0.835 (0.727\u0026ndash;0.959), P\u0026thinsp;=\u0026thinsp;0.011; AT VO₂/ kg_ TyG: 0.870 (0.766\u0026ndash;0.990), P\u0026thinsp;=\u0026thinsp;0.034. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \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\u003eAssociations of TyG index and modified TyG index with MACE onset\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eindex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003etertile 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003etertile 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003etertile 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\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\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.011\u0026ndash;1.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.022\u0026ndash;1.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.021\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.013\u0026ndash;1.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.012\u0026ndash;1.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.031\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.024\u0026ndash;1.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.027\u0026ndash;1.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.018\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epeak VO\u003csub\u003e2\u003c/sub\u003e/kg_ TyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.772\u0026ndash;0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.741\u0026ndash;0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.768\u0026ndash;0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.726\u0026ndash;0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.759\u0026ndash;0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.711\u0026ndash;0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epeak O\u003csub\u003e2\u003c/sub\u003e pulse_ TyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.836\u0026ndash;1.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.755\u0026ndash;0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.013\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.819\u0026ndash;1.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.724\u0026ndash;0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.822\u0026ndash;1.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.727\u0026ndash;0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT VO\u003csub\u003e2\u003c/sub\u003e/kg_ TyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.864\u0026ndash;1.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.771\u0026ndash;0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.031\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.867-1.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.774\u0026ndash;0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.042\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.866\u0026ndash;1.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.766\u0026ndash;0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.034\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: HR, hazard ratio; CI, confidence interval; TyG, triglyceride-glucose index; Peak VO\u003csub\u003e2\u003c/sub\u003e/kg, peak volume of oxygen per kilogram; Peak O\u003csub\u003e2\u003c/sub\u003e pulse, peak oxygen pulse; AT VO\u003csub\u003e2\u003c/sub\u003e/kg, anaerobic threshold volume of oxygen per kilogram.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e* Indicates a statistically significant difference between tertile 1 and tertile 2 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e#\u003c/sup\u003e Indicates a statistically significant difference between tertile 1 and tertile 3(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 1: unadjusted; model 2: age and sex were adjusted; Model 3: age, sex, hypertension, diabetes, smoking status and drinking status were adjusted. Model 2 was adjusted for age and sex. Given that age and sex are fundamental, unmodifiable cardiovascular risk variables, they were routinely adjusterd as covariates in studies assessing the prognostic value of the TyG index\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Furthermore, given the natural decline in cardiorespiratory fitness with age and the generally superior core CPET metrics in males compared to females, adjustment for age and sex is an essential step to minimize relevant confounding bias\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e In addition to age and sex, model 3 was adjusted for smoking status, drinking status, hypertension, and diabetes. These variables were included to control for potential confounding from traditional cardiovascular risk factors. Similar adjustment protocols have been employed in prior research\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePredictive capacity comparison\u003c/h3\u003e\n\u003cp\u003eThe study calculated time-dependent Harrell\u0026rsquo;s C-indices for four TyG-related parameters: TyG, peak VO\u003csub\u003e2\u003c/sub\u003e/kg_ TyG, peak O\u003csub\u003e2\u003c/sub\u003e pulse_ TyG and AT VO\u003csub\u003e2\u003c/sub\u003e/kg_ TyG. These indices showed significant predictive value for MACE (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The C-index values (95% CI) were: 0.565 (0.563\u0026ndash;0.568) for TyG; 0.579 (0.577\u0026ndash;0.581) for peak VO\u003csub\u003e2\u003c/sub\u003e/kg_ TyG; 0.565 (0.563\u0026ndash;0.567) for peak O\u003csub\u003e2\u003c/sub\u003e pulse_ TyG; and 0.569 (0.568\u0026ndash;0.571) for AT VO\u003csub\u003e2\u003c/sub\u003e/kg_ TyG. The time-dependent AUC indicated that the TyG index and its modified indices (including peak VO₂/kg_ TyG, peak O₂ pulse_ TyG, and AT VO₂/kg_ TyG) consistently exhibited significant predictive value for MACE throughout the follow-up period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eStratified Analysis\u003c/h3\u003e\n\u003cp\u003eIn Scenario 1, participants were stratified by median peak VO₂/kg. Among those with above-median peak VO₂/kg, a higher TyG index (above median) was associated with an increased risk of MACE (HR: 1.199, 95% CI: 1.027-1.400, P\u0026thinsp;=\u0026thinsp;0.022). Similarly, in the group with below-median peak VO₂/kg, a higher TyG index (above median) also significantly increased the risk of MACE (HR: 1.162, 95% CI: 1.007\u0026ndash;1.341, P\u0026thinsp;=\u0026thinsp;0.040). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\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\u003eRisk reclassification of MACE based on the TyG index and peakVO\u003csub\u003e2\u003c/sub\u003ekg\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003eScenario 1\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epeak VO\u003csub\u003e2\u003c/sub\u003e/kg \u0026gt;median\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u0026lt;median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u0026gt;median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.027-1.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epeak VO\u003csub\u003e2\u003c/sub\u003e/kg \u0026lt;median\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u0026lt;median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u0026gt;median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.007\u0026ndash;1.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario 2\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u0026lt;median\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epeak VO\u003csub\u003e2\u003c/sub\u003e/kg \u0026gt;median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epeak VO\u003csub\u003e2\u003c/sub\u003e/kg \u0026lt;median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.016\u0026ndash;1.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u0026gt;median\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epeak VO\u003csub\u003e2\u003c/sub\u003e/kg \u0026gt;median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epeak VO\u003csub\u003e2\u003c/sub\u003e/kg \u0026lt;median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.984\u0026ndash;1.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: Hazard ratios (HR) and 95% confidence intervals (CI) were derived from Cox proportional hazards models. Median values were used as cut-off points for both the TyG index and peak VO2/kg \u0026lt;median.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e*\u003c/sup\u003e Indicates a statistically significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to the TyG\u0026thinsp;\u0026lt;\u0026thinsp;median reference group within the same aerobic capacity stratum.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e#\u003c/sup\u003e Indicates a statistically significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to the peak VO\u003csub\u003e2\u003c/sub\u003e/kg \u0026lt;median reference group within the same TyG index stratum.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eTyG, triglyceride-glucose index; peak VO\u003csub\u003e2\u003c/sub\u003e/kg, peak oxygen uptake per kilogram; MACE, major adverse cardiovascular events; HR, hazard ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eScenario 1: Association between TyG and MACE across peak VO\u003csub\u003e2\u003c/sub\u003e/kg groups; Scenario 2: association between peak VO\u003csub\u003e2\u003c/sub\u003e/kg and MACE across TyG groups\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Scenario 2, stratification was performed by the TyG index. Participants with a below-median TyG index and low peak VO₂/kg (below median) had a significantly higher risk of MACE (HR: 1.186, 95% CI: 1.016\u0026ndash;1.384, P\u0026thinsp;=\u0026thinsp;0.030). However, among those with an above-median TyG index, low peak VO₂/kg was not significantly associated with an increased risk of MACE (HR: 1.142, 95% CI: 0.984\u0026ndash;1.327, P\u0026thinsp;=\u0026thinsp;0.081). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMediation analysis\u003c/h2\u003e \u003cp\u003eIn the model 3 (age, sex, hypertension, diabetes, smoking status and drinking status were adjusted) with the TyG index as the independent variable, the average causal mediation effect (ACME) was 0.00376 (95% CI 0.00217-0.0100; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The average direct effect (ADE) was 0.01726 (P\u0026thinsp;=\u0026thinsp;0.008). Consequently, the proportion of the total effect mediated was 17.90% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When peak VO₂/kg was included as the independent variable, the ACME was \u0026minus;\u0026thinsp;0.0000737 (95% CI -0.000286-0.0000; P\u0026thinsp;=\u0026thinsp;0.520), while the ADE was \u0026minus;\u0026thinsp;0.0048 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Thus, the mediated proportion was 1.51% (P\u0026thinsp;=\u0026thinsp;0.520). (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study innovatively integrated the TyG index with key CPET parameters to develop modified TyG indices. The study subsequently evaluated and compared the predictive value of the traditional TyG index versus these novel indices for MACE. This study analysis demonstrated that although the TyG index and the modified indices showed only a weak-to-moderate correlation, both were independent predictors of MACE risk. Notably, for MACE risk stratification, the peak VO₂/kg_TyG and the AT VO₂/kg_TyG presented significantly superior predictive accuracy compared to the traditional TyG index, with the peak VO₂/kg_TyG index performing the best. Moreover, mediation analysis revealed that peak VO₂/kg partially mediated the association between the TyG index and MACE.\u003c/p\u003e \u003cp\u003eThe TyG index has been extensively validated as an independent predictor of cardiovascular outcomes. For instance, a large retrospective cohort study by Lee et al. confirmed that elevated TyG levels independently predict all-cause and cardiovascular mortality[8]. This is further supported by meta-analytic evidence, which synthesizes data from multiple studies to confirm significant associations between the TyG index and the risks of cardiovascular events, all-cause mortality, and cardiovascular-specific mortality\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Moreover, longitudinal changes in the TyG index (including its cumulative burden, fluctuation, and rising trend) have also been linked to a higher incidence of cardiovascular events\u003csup\u003e[\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. In this regard, our findings regarding the predictive value of the traditional TyG index align with the established body of evidence.\u003c/p\u003e \u003cp\u003eCPET has emerged in recent years as the gold-standard, non-invasive method for comprehensively assessing cardiorespiratory fitness, gas exchange efficiency, and exercise tolerance, providing established benchmarks for evaluating cardiovascular functional reserve\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Consequently, the present study innovatively integrated the TyG index, reflecting basal metabolic state, with key CPET parameters representing dynamic cardiorespiratory response to construct a novel modified TyG indices. Peak VO₂/kg, peak O₂ pulse, and AT VO₂/kg serve as key indicators of cardiorespiratory fitness\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. These parameters directly quantify the overall efficiency of oxygen transport and utilization dynamics under exercise stress[29]. Although physiological interplay exists between cardiorespiratory function and the glucolipid metabolic dysregulation reflected by the TyG index, these systems occupy distinct physiological domains: the former governs the delivery and utilization of energetic substrates, while the latter primarily regulates their metabolism. Consistent with this conceptual distinction, the study presented only a weak correlation between the TyG index and the modified TyG indices. This key finding indicates that the modified TyG indices do not merely amplify or replicate the metabolic information of the original index. Instead, they structurally incorporate a novel dimension of cardiorespiratory reserve, thereby creating a more comprehensive and integrative risk assessment tool.\u003c/p\u003e \u003cp\u003eThe present study confirmed that both the TyG index and the modified TyG indices were independent predictors of the long-term risk of MACE. Notably, with regard to predictive performance, both peak VO₂/kg_ TyG and AT VO₂/kg_ TyG significantly outperformed the TyG index, with peak VO₂/kg_ TyG presented the most prominent superiority.\u003c/p\u003e \u003cp\u003eThe underlying mechanism for this enhanced predictive capacity can be primarily attributed to the rich pathophysiological information captured by peak VO₂/kg. Specifically, peak VO₂/kg is a comprehensive indicator reflecting overall cardiopulmonary function, not only assessing a patient's maximum cardiac pumping capacity and functional reserve but also providing an integrated assessment of cardiovascular disease severity\u003csup\u003e[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Moreover, robust clinical evidence confirms a clear association between peak VO₂/kg levels and cardiovascular mortality, where an improvement in peak VO₂/kg is indicative of enhanced cardiac function and is associated with a corresponding reduction in cardiovascular mortality\u003csup\u003e[\u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Precisely because of the high clinical predictive value inherent in peak VO₂/kg itself and its reflection of the integrated health status of the cardiovascular system, its incorporation into the TyG index significantly augmented the model\u0026rsquo;s predictive capacity for MACE risk.\u003c/p\u003e \u003cp\u003eIn addition, the mediation analysis reveals distinct mechanistic pathways for the TyG index and peak VO₂/kg. At the level of cardiac energy metabolism, while the myocardium preferentially utilizes fatty acids for energy production, it possesses the inherent capacity to adapt by switching ATP generation pathways in response to substrate availability to maintain energy homeostasis\u003csup\u003e[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. However, insulin resistance disrupts this metabolic flexibility. It forces the heart into an excessive reliance on fatty acid oxidation, particularly impairing the crucial shift from fatty acid to glucose utilization that should occur under stress conditions, such as myocardial injury[41]. This metabolic inflexibility results in increased myocardial lipid uptake and accumulation, inducing lipotoxicity\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. CPET can provide a critical reference for this assessment. It precisely quantifies the threshold for myocardial metabolic transition by effectively assessing the balance between myocardial metabolic demand and supply during exercise. Therefore, CPET combined with an evaluation of insulin resistance status, provides a more accurate assessment of cardiac metabolic reserve capacity. This integrated approach offers a powerful tool for the diagnosis and risk stratification of metabolic heart disease.\u003c/p\u003e \u003cp\u003eThe present study provides a novel method for predicting the risk of MACE in ACS patients following PCI. Nevertheless, the predictive performance and the underlying mechanisms of this model require validation in prospective cohort studies and fundamental research.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eSeveral limitations of this study should be acknowledged. First, the retrospective design inherently carries risks of selection and information bias, despite the application of rigorous inclusion/exclusion criteria and thorough data cleaning. Second, the absence of prospective controls and interventions precludes establishing a causal relationship between the TyG index and MACE. Third, although adjustments were made for multiple known cardiovascular risk factors, the influence of unmeasured confounders such as inflammatory states, medication adherence, and lifestyle factors cannot be entirely excluded. Another limitation lies in the dependence of CPET parameters on patient cooperation and the consistency of test administration. Despite all tests being conducted by experienced technicians under standardized protocols, CPET results may still be affected by subjective effort and exercise tolerance. First, the retrospective design inherently carries risks of selection and information bias, despite the application of rigorous inclusion/exclusion criteria and thorough data cleaning.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe TyG index and the modified TyG indices presented significantly independent associations with MACE in the study population. Peak VO₂/kg_ TyG presented significantly higher predictive accuracy for MACE risk stratification compared with the TyG index. In addition, peak VO₂/kg significantly mediates the association between the TyG index and MACE.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics declarations\u003c/h2\u003e \u003cp\u003e The Ethics Committee of General Hospital of Northern Theater Command approved the research protocol [Y (2025)444].\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent to participate\u003c/h2\u003e \u003cp\u003e Informed consent was obtained from all participants or their legally authorized representative.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by the Liaoning Province Science \u0026amp; Technology Project (2025JH2/101800045). The funder had no role in the design of the study, collection and analysis of the data, and writing of the paper.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYaling Han conceived the study concept. Yaling Han and Quanyu Zhang designed the trial. Yanchun Liang and Jian Zhang provided critical appraisal of the study. Yan-Xia Wang, Cai-Lian Wang, Yi Zhang, Lin Pang and Sheng-Yi Wang oversaw the cardiac rehabilitation program. SiCheng Ning and Ning Sun conducted the research and were responsible for data collection and curation. SiCheng Ning and Muxing Bo performed the statistical analysis, while Muxing Bo handled validation and visualization. SiCheng Ning drafted the manuscript, and Quanyu Zhang critically revised it. All authors approved the final version and agree to be accountable for all aspects of the work, ensuring its integrity and accuracy.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe thank the patients and family for their participation.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to confidentiality restrictions but can be made available by the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWu Z, Zhou D, Liu Y, et al. Association of TyG index and TG/HDL-C ratio with arterial stiffness progression in a non-normotensive population. 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Proc Natl Acad Sci U S A. 2000;97(4):1784\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.97.4.1784\u003c/span\u003e\u003cspan address=\"10.1073/pnas.97.4.1784\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cardiac Rehabilitation (CR), Cardiopulmonary Exercise Testing (CPET), Triglyceride-Glucose Index (TyG)","lastPublishedDoi":"10.21203/rs.3.rs-8868777/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8868777/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe TyG index, serving as a convenient alternative marker for IR, has been established as correlating with cardiovascular disease risk. The TyG index also demonstrates robust predictive capacity for cardiovascular outcomes. However, by depending solely on biomarkers, the TyG index fails to reflect critical functional states, including cardiopulmonary reserve. In contrast, CPET has been established as an objective, quantitative functional assessment tool for evaluating cardiorespiratory fitness in patients undergoing PCI, while providing evidence for prognostic risk stratification. The present study aimed to develop the modified TyG indices by combining the TyG index with CPET parameters and to compare their predictive performance for MACE against that of the original TyG index.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThe study used Cox proportional hazards regression to analyze the association and predictive capacity based on hazard ratio and Harrell\u0026rsquo;s C-index. Mediation analysis was performed to quantify the mediation effect.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eThe study enrolled 8,803 ACS patients who underwent CPET after PCI. The mean participant age was 57.39\u0026thinsp;\u0026plusmn;\u0026thinsp;8.97 years, and 2,032 participants (23.1%) were female. Over a median follow-up of 5.0 years, 1,548 MACE events occurred (17.6%). For each index, including the TyG index and its modified versions (peak VO₂/kg_ TyG, AT VO₂/kg_ TyG, and peak O₂ pulse_ TyG), participants were independently divided into tertiles (T1\u0026ndash;T3) to evaluate their respective associations with MACE. Compared with the lowest tertile, the highest tertile was associated with the following HRs for MACE: TyG, 1.169 (95% CI, 1.027\u0026ndash;1.330); peak VO₂/kg_ TyG, 0.814 (95% CI, 0.711\u0026ndash;0.932); peak O₂ pulse_ TyG, 0.835 (95% CI, 0.727\u0026ndash;0.959); and AT VO₂/kg_ TyG, 0.870 (95% CI, 0.766\u0026ndash;0.990). The C-index values for peak VO₂/kg_ TyG and AT VO₂/kg_ TyG for MACE were higher than that of the TyG index. Mediation analysis was employed to delineate these relationships. Peak VO₂/kg mediated 17.9% of the association of the TyG index with MACE. In contrast, TyG mediated only 1.5% of the association of peak VO₂/kg with MACE.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn summary, peak VO₂/kg_ TyG showed significantly improved predictive accuracy MACE risk stratification in ACS patients compared to the traditional TyG index.\u003c/p\u003e","manuscriptTitle":"Predictive Value of Triglyceride-Glucose (TyG) Index and the Modified TyG Index combined with Cardiopulmonary Exercise Testing (CPET) Parameters for Major Adverse Cardiovascular Events (MACE)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-23 10:17:21","doi":"10.21203/rs.3.rs-8868777/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"082aaae8-5589-4cc9-917d-405b2910f1c5","owner":[],"postedDate":"March 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T08:28:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-23 10:17:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8868777","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8868777","identity":"rs-8868777","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}