Predictive Value of the Combined TyG and FI Index for Cardiovascular Disease and Stroke in two prospective cohorts

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The combined impact of TyG-Frailty Index (TyGFI) has not been adequately explored. This study aimed to investigate the association between TyGFI and the risk of cardiovascular disease (CVD) and stroke. Methods A total of 5,448 participants from the China Health and Retirement Longitudinal Study (CHARLS) and 1,139 participants from the U.S. National Health and Nutrition Examination Survey (NHANES) were included. Multivariable logistic regression models were used to estimate associations with CVD and stroke, adjusting for demographic, clinical, and lifestyle covariates. Restricted cubic spline (RCS) and subgroup analyses were employed to examine dose-response relationships and interaction effects. Results Higher TyGFI levels were associated with older age, adverse metabolic parameters, and increased prevalence of hypertension, diabetes, and dyslipidemia. In fully adjusted models, the highest TyGFI quartile was significantly associated with increased risks of CVD (CHARLS: OR 15.09, 95% CI: 9.65–23.60; NHANES: OR 4.98, 95% CI: 2.04–12.19) and stroke (CHARLS: OR 21.12, 95% CI: 6.44–69.23; NHANES: OR 12.98, 95% CI: 2.58–65.17), with consistent dose-response trends confirmed by RCS analyses. Subgroup analyses further demonstrated the robustness of these associations across diverse demographic and clinical strata. Conclusions TyGFI is a strong and independent predictor of CVD and stroke in two nationally representative cohorts. By integrating metabolic and functional risk dimensions, TyGFI provides a more comprehensive risk stratification tool, with significant implications for early identification and prevention of cardiovascular events in aging populations. Triglyceride-glucose index Frailty index Cardiovascular disease Stroke Population-based cohort Figures Figure 1 Figure 2 Figure 3 1. Introduction Cardiovascular disease (CVD) and stroke remain the leading causes of mortality and disability worldwide, posing a substantial burden on global public health. Over 17 million people die each year from CVD, accounting for nearly one-third of all global deaths, with stroke ranking as the second leading cause of death and exhibiting a concerning shift toward younger populations(1, 2). In China, stroke has become the leading cause of death and disability, with approximately 17.8 million prevalent cases, 3.4 million new cases annually, and 2.3 million stroke-related deaths reported in 2020(3, 4). As the aging population grows, the incidence and mortality rates of CVD and stroke continue to rise among older adults, underscoring the urgent need for more refined risk assessment tools to enable early prevention and intervention(5). The triglyceride-glucose (TyG) index has emerged in recent years as a reliable and accessible surrogate marker of insulin resistance. A growing body of literature has confirmed its predictive value for metabolic disorders and atherosclerotic diseases, including diabetes, coronary artery disease, heart failure, and stroke(6-9). In addition to these primary outcomes, elevated TyG levels have also been associated with complex vascular outcomes such as carotid plaque progression, cardiovascular mortality, and post-stroke depression, expanding its utility in clinical risk stratification(10, 11). In parallel, frailty index (FI)—a multidimensional clinical syndrome characterized by reduced physiological reserve and increased vulnerability to stressors—has been independently linked to a range of adverse outcomes, including CVD, stroke, falls, cognitive decline, and all-cause mortality(12-14). The high prevalence of frailty in older adults and its predictive relevance for poor health outcomes make it an essential component of geriatric risk models. Recent evidence has shown that worsening frailty is associated with increased CVD risk, whereas frailty remission may correspond with lower event rates(15). Despite the established individual roles of TyG and frailty in predicting cardiovascular risk, they have largely been assessed in isolation. Few studies have explored their combined effect, even though metabolic dysfunction and physiological vulnerability frequently coexist in older adults. These coexisting conditions may act synergistically through mechanisms such as chronic inflammation, oxidative stress, and endothelial dysfunction to promote vascular damage and accelerate atherosclerosis(16, 17). Moreover, traditional risk prediction models—such as the Framingham Risk Score—do not adequately incorporate aging-related variables such as frailty or low-grade inflammation, limiting their accuracy in elderly populations. To address this gap, the present study introduces a novel composite risk indicator—the TyG-Frailty Index (TyGFI)—designed to capture the dual burden of metabolic stress and physiological vulnerability. Using data from two nationally representative cohorts, the China Health and Retirement Longitudinal Study (CHARLS) and the U.S. National Health and Nutrition Examination Survey (NHANES), we systematically examine the associations between TyGFI and the risks of CVD and stroke. Through multivariable logistic regression, restricted cubic spline (RCS) modeling, and stratified subgroup analyses, this study aims to evaluate the independent and joint predictive value of TyGFI. Our findings have the potential to inform a more nuanced and integrative approach to cardiovascular risk assessment, particularly among older adults with high multimorbidity burden. 2. Methods Study Design Overview This study conducted a comprehensive analysis of the association between the combined TyG and FI and the incidence of CVD and stroke, adjusting for multiple potential confounders. Data were obtained from two nationally cohorts: the NHANES and the CHARLS. The findings were rigorously validated through sensitivity analyses. Detailed procedures for data extraction from the two cohorts are depicted in the flowcharts (Figure 1). The CHARLS cohort initially comprised 17,596 participants from the 2011 survey. The final analytical sample included 5,448 participants, stratified into four quartiles (n=1,358–1,369). Meanwhile, the NHANES cohort yielded a final sample of 1,139 participants, also divided into quartiles (n=282–287). Two Extensive Observational Cohort Studies NHANES Database Cohort: NHANES is a comprehensive health examination and nutritional assessment survey of adults and children conducted in the United States. The database includes demographic information, dietary habits, laboratory tests, physical examinations, and questionnaire responses collected between 2001 and 2023. The NHANES study protocol received approval from the Ethics Review Board of the National Center for Health Statistics, and written informed consent was obtained from all participants prior to data collection. CHARLS Database Cohort: Data were also sourced from CHARLS, a nationally representative longitudinal survey designed to capture health and economic data among China's elderly population. CHARLS covers demographic information, physical health assessments, cognitive tests, biomarkers, and socioeconomic indicators. Ethical approval for CHARLS was provided by the Institutional Review Board at Peking University, and informed consent was obtained from all study participants. Data collection and definitions The CHARLS dataset encompassed data collected between 2011 and 2015, while the NHANES dataset covered the period from 2001 to 2023, with baseline data specifically restricted to 2011 or earlier. Collected variables included demographic characteristics such as age, sex, and education level. Clinical measurements recorded included systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), glucose, hemoglobin A1c (HbA1c), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDLC), and low-density lipoprotein cholesterol (LDLC). Lifestyle factors such as smoking and drinking status were documented, alongside medical histories of dyslipidemia, hypertension, and diabetes mellitus (DM). The TyG index was calculated using the formula: Ln [triglycerides (mg/dl) × glucose (mg/dl)/2]. Frailty was measured in relation to the accumulation of deficits using FI(18). The combined TyG and FI(18) was computed using: TyGFI = TyG × FI. Statistical analysis Descriptive statistics were conducted to summarize demographic and clinical characteristics. Continuous variables were summarized as means ± standard deviations. Categorical variables were summarized as frequencies and percentages. Logistic regression analyses were employed to evaluate associations between the combined TyGFI and outcomes CVD and stroke with adjustments for potential confounders. Both continuous TyGFI and categorical TyGFI (median-based qua。、GG？Rtiles) were analyzed, with odds ratios (ORs), 95% confidence intervals (CIs), and p-values reported. Restricted cubic spline (RCS) analysis was applied to evaluate nonlinear associations, visualized through plots indicating the probability of CVD and stroke. Stratified subgroup analyses were performed based on variables such as sex, smoking status, drinking status, hypertension, DM, dyslipidemia, and education. Results were visualized using forest plots. The study strictly adhered to STROBE guidelines and accounted for NHANES survey design and sampling weights. 3. Results Baseline characteristics of the study population According to the inclusion and exclusion criteria, a total of 5448 participants from the CHARLS cohort (female: 53.71%, mean age: 58.65 years) and 1139 from the NHANES cohort (female: 52.74%, mean age: 60.96 years) were included in the baseline analyses. Detailed participant characteristics are summarized in Table 1. Participants were categorized into quartiles (Q1–Q4) based on their TyGFI levels. Across both cohorts, individuals in higher TyGFI quartiles tended to be older and exhibited more adverse metabolic profiles. In CHARLS, average age increased from 56.4 in Q1 to 61.9 years in Q4, and in NHANES from 59.1 to 62.8 years. BMI followed a similar trend, rising from 23.2 to 23.9 kg/m² in CHARLS and from 27.6 to 31.7 kg/m² in NHANES. Metabolic biomarkers also showed significant changes. Fasting glucose increased from 104.7 to 116.7 mg/dL in CHARLS and from 100.9 to 128.2 mg/dL in NHANES. HbA1c rose from 5.1% to 5.4% in CHARLS and from 5.5% to 6.4% in NHANES. In both cohorts, TG, TC, and LDLC increased across TyGFI quartiles, while HDLC declined. Prevalence of chronic conditions also increased with TyGFI. In CHARLS, hypertension rose from 21% in Q1 to 56% in Q4, dyslipidemia from 34% to 46%, and DM from 8% to 22%. Similar increases were observed in NHANES, with DM rising from 25% to 64%. Smoking prevalence and lower educational attainment were more common in higher TyGFI groups. Notably, drinking frequency declined with TyGFI in CHARLS but showed no consistent pattern in NHANES. Association between TyGFI index and cardiovascular disease risk The association between the TyGFI and the risk of CVD was evaluated in both the CHARLS and NHANES cohorts using logistic regression models with increasing levels of covariate adjustment (Table 2). In the CHARLS cohort, a strong and graded association was observed between TyGFI quartiles and CVD risk. Compared to participants in the lowest quartile (Q1), those in Q2, Q3, and Q4 showed significantly elevated odds of cardiovascular events. In the fully adjusted model (Model 3), the odds ratios (ORs) for CVD were 3.55 (95% CI: 2.21–5.70) in Q2, 7.77 (95% CI: 4.96–12.18) in Q3, and 15.09 (95% CI: 9.65–23.60) in Q4 (p for trend <0.0001), which were consistent across all models. In the NHANES cohort, similar trends were observed, though with slightly attenuated effect sizes. In Model 3, compared with Q2 (reference), Q3 had an OR of 3.41 (95% CI: 1.36–8.56) and Q4 had an OR of 4.98 (95% CI: 2.04–12.19), both statistically significant. The p for trend remained highly significant (p <0.001) in all models except Model 2 (p = 0.11), which still showed elevated point estimates. When TyGFI was analyzed as a continuous variable using its median value, the association with CVD remained robust across all models in both cohorts. After adjusting for age, sex, lifestyle factors, blood pressure, glycemic and lipid parameters, and socioeconomic variables (Model 3), TyGFI remained independently associated with increased cardiovascular risk. These findings from two nationally representative cohorts underscore a consistent and independent relationship between higher TyGFI and elevated risk of CVD, reinforcing the potential clinical utility of TyGFI as a composite marker for cardiovascular risk stratification as also shown in Table 2. Subgroup analysis for TyGFI index and cardiovascular disease risk Subgroup analyses were conducted for both the CHARLS and NHANES cohorts, focusing on the association between TyGFI and CVD risk. The results from the CHARLS cohort (Fig. 2A) demonstrated a significant and consistent increase in CVD risk across TyGFI quartiles, with stronger associations observed among males, smokers, drinkers, and individuals with hypertension, diabetes mellitus (DM), and dyslipidemia. Participants with lower education levels also exhibited higher risk estimates. The RCS analysis (Fig. 2C) indicated a nonlinear positive association between TyGFI and CVD risk, further reinforcing the findings from the categorical quartile analysis. Similarly, in the NHANES cohort (Fig. 2B), a stepwise increase in CVD risk was observed with higher TyGFI quartiles. The subgroup analysis revealed particularly strong associations among smokers, drinkers, and individuals with hypertension, DM, and dyslipidemia. Educational disparities were also noted, with lower education levels correlating with elevated CVD risk. RCS analysis (Fig. 2D) confirmed the nonlinear relationship between TyGFI and CVD, demonstrating a consistent trend across different adjustment models. Association between TyGFI index and stroke risk Table 3 presents the associations between the TyGFI and stroke across the CHARLS and NHANES cohorts. In both cohorts, higher TyGFI levels were significantly associated with an increased risk of stroke. In the CHARLS cohort, each unit increase in TyGFI was associated with a markedly elevated risk of stroke across all models. In the fully adjusted Model 3, the hazard ratio (HR) was 8.99 (95% CI: 5.53–14.62, p < 0.0001). When TyGFI was analyzed by quartiles, a clear dose-response relationship was observed. Compared to the reference group (Q1), individuals in Q3 and Q4 had significantly higher risks of stroke in all models. In Model 3, the OR were 8.22 (95% CI: 2.45–27.58, p < 0.001) for Q3 and 21.12 (95% CI: 6.44–69.23, p < 0.0001) for Q4, with a significant trend across quartiles (p for trend < 0.0001). Similar patterns were observed in the NHANES cohort. Although the associations were somewhat attenuated compared to CHARLS, the trend remained significant. In the fully adjusted Model 3, the OR for Q3 and Q4 were 6.36 (95% CI: 1.44–28.00, p = 0.02) and 12.98 (95% CI: 2.58–65.17, p = 0.004), respectively, with a consistent trend across quartiles (p for trend < 0.001). The OR for Q2 in both cohorts did not reach statistical significance. Subgroup analysis for TyGFI index and stroke risk Subgroup analyses were also performed to assess the relationship between TyGFI and stroke risk across different demographic and clinical subgroups within the CHARLS and NHANES cohorts. In the CHARLS cohort (Fig. 3A), a clear trend emerged, showing a significant escalation in stroke risk as TyGFI quartiles increased. Compared with participants in the lowest quartile, those in Q4 exhibited the highest stroke risk, particularly among males, individuals who consumed alcohol, and those diagnosed with dyslipidemia. Males in Q4 had markedly greater odds of stroke (OR 42.47, 95% CI: 9.067–157.63, p<0.001), as did drinkers (OR 18.48, 95% CI: 3.619–347.36, p=0.005) and participants with dyslipidemia (OR 23.06, 95% CI: 4.918–411.43, p=0.002). The RCS analysis (Fig. 3C) further illustrated a nonlinear association, demonstrating an accelerating increase in stroke probability at higher TyGFI levels. Findings from the NHANES cohort (Fig. 3B) were consistent, revealing a stepwise elevation in stroke risk with increasing TyGFI quartiles. The association was particularly pronounced among those with hypertension, dyslipidemia, and lower educational attainment. Participants with hypertension in Q4 experienced a significantly heightened risk (OR 11.34, 95% CI: 2.526–79.023, p=0.004), as did those with dyslipidemia (OR 14.98, 95% CI: 5.375–62.349, p<0.001). Moreover, individuals with only primary school education demonstrated a stronger association with stroke risk (OR 14.25, 95% CI: 0.855–259.03, p=0.010). The RCS analysis (Fig. 3D) corroborated these findings, confirming a nonlinear dose-response relationship between TyGFI and stroke risk, with a sharp increase in probability observed at higher TyGFI values. 4. Discussion This investigation elucidates a robust and independent relationship between the combined TyG and FI indices and the risk of both CVD and stroke. Utilizing two large, nationally representative cohorts—CHARLS and NHANES—our analysis demonstrates that elevated TyGFI levels are consistently associated with increased odds of adverse cardiovascular outcomes. Furthermore, this association exhibits a clear dose-response pattern, as participants within the highest TyGFI quartiles experienced significantly greater risk elevations. Importantly, these findings remained stable after rigorous adjustment for a wide array of demographic, clinical, and lifestyle confounders. The observed associations were further corroborated through subgroup analyses, which revealed consistent effects across diverse demographic and clinical strata. Collectively, these results emphasize the potential clinical relevance of the TyGFI as an integrative risk marker for cardiovascular disease prevention and management. The association between higher TyGFI levels and increased risks of CVD and stroke likely reflects the combined effects of metabolic dysfunction and frailty on vascular health. Individuals in higher TyGFI quartiles had worse metabolic profiles, including higher BMI, glucose, HbA1c, and unfavorable lipid levels—all known risk factors for atherosclerosis. The clear dose-response pattern in both CHARLS and NHANES supports TyGFI as a reliable tool for cardiovascular risk stratification, even after adjusting for various confounders. Subgroup analyses further show that TyGFI can amplify risk in the presence of traditional factors like hypertension, diabetes, and dyslipidemia. The consistent findings across two large and diverse cohorts highlight TyGFI's broad applicability and potential clinical value. Extensive research has established the TyG as a reliable surrogate marker for insulin resistance and a significant predictor of adverse cardiovascular and cerebrovascular outcomes. Prior studies have consistently demonstrated that elevated TyG levels are associated with increased risks of atherosclerotic cardiovascular disease (ASCVD), stroke, and mortality( 8 , 19 – 22 ). Ding et al. (2021) confirmed that individuals with higher TyG levels were more likely to develop cardiovascular events, even in the absence of baseline CVD( 23 ). Cui et al. further highlighted the prognostic value of TyG, particularly in populations with compromised renal function( 24 ). In addition, Chen et al. reported a strong association between TyG and both all-cause and cardiovascular mortality( 21 ). In the domain of stroke research, elevated TyG has also been linked to increased incidence, recurrence, and poor prognosis. Yang et al. identified a significant association between higher TyG levels and the risk of ischemic stroke and its recurrence( 25 ), while Cai et al. demonstrated that TyG was independently associated with in-hospital and ICU mortality in patients with critical stroke( 19 ). Moreover, longitudinal analyses conducted by Wu et al. and Huang et al. emphasized that persistent or increasing TyG trajectories over time were significantly correlated with elevated stroke risk( 15 , 26 , 27 ). These findings underscore the importance of TyG as a long-term indicator of metabolic risk. Emerging evidence has also begun to clarify the potential mechanisms behind these associations. Huo et al. showed that TyG mediated a substantial proportion of the relationship between body mass index and stroke, suggesting its involvement in key pathophysiological pathways linking obesity to vascular injury( 17 ). Although previous studies have clearly established the independent role of TyG in predicting cardiovascular and cerebrovascular events, the present study contributes a novel perspective by incorporating frailty—a frequently overlooked, yet clinically relevant, determinant of cardiovascular risk. Recent findings by He et al. demonstrated that progression in frailty status significantly increased the risk of cardiovascular events, independent of traditional metabolic risk factors( 15 ). However, few investigations have attempted to combine metabolic and functional indicators into a single index. Overall, this study exhibits multiple methodological and conceptual strengths that reinforce the robustness, generalizability, and clinical relevance of its findings. Notably, the utilization of two nationally representative and demographically distinct cohorts—CHARLS from China and NHANES from the United States—ensures broad external validity across populations with diverse sociocultural, genetic, and healthcare backgrounds. The consistency of results across these cohorts enhances the credibility of the observed associations and addresses an important limitation in prior cardiovascular research( 22 , 24 , 28 , 29 ). Moreover, the study advances the field by proposing a novel composite risk indicator—TyGFI—that integrates the well-validated TyG, a surrogate marker of insulin resistance, with FI, a widely accepted measure of cumulative physiological deficits. While both TyG and FI have been independently associated with cardiovascular and cerebrovascular outcomes( 15 , 30 , 31 ), their combination into a single metric represents a conceptual innovation. This dual-domain approach captures both metabolic and functional deterioration, enabling a more holistic and sensitive assessment of cardiovascular risk, particularly among aging individuals. In addition, the analytical framework is characterized by rigorous adjustment for a comprehensive set of covariates, including demographic, socioeconomic, behavioral, and clinical factors. The application of multivariable logistic regression, restricted cubic spline modeling, and stratified subgroup analyses ensures statistical robustness and allows for the detection of both linear and nonlinear relationships. Besides, the study also contributes to the literature by empirically demonstrating the added predictive value of TyGFI over its individual components. In doing so, it addresses a critical gap identified in previous studies that evaluated isolated metabolic indicators, such as TyG-BMI and TyG-WHtR, without accounting for functional health status( 17 , 32 ). The integrative nature of TyGFI offers a more refined stratification tool for identifying individuals at elevated risk of cardiovascular and cerebrovascular events. Therefore, these strengths underscore the originality and applicability of TyGFI as a multidimensional risk marker, offering significant potential for incorporation into precision prevention strategies for cardiovascular and cerebrovascular disease. However, despite the methodological rigor and cross-cohort validation, several limitations of this study should be acknowledged, particularly in the context of temporal dynamics and evolving risk factors. First, the analysis was based on baseline measurements of the TyG and FI, which are both time-sensitive indicators. However, growing evidence emphasizes the importance of longitudinal changes and cumulative exposure to these markers. For instance, Wu et al. and Huang et al. demonstrated that persistent elevation or upward trajectories in TyG over time were significantly associated with increased stroke risk in middle-aged and older adults( 26 , 27 ). Similarly, He et al. showed that progression in frailty status over time was closely linked with elevated CVD incidence( 15 ). Second, although the study employed multivariable adjustment to control for confounding factors, its observational nature precludes definitive causal inference. While the associations identified are robust and consistent across two nationally representative cohorts, unmeasured confounding remains a possibility. Incorporating analytical approaches such as Mendelian randomization or instrumental variable analysis could strengthen causal interpretations, as illustrated by Jiang et al.( 33 ). Third, although CHARLS and NHANES represent different sociocultural and healthcare contexts, generalizability beyond Chinese and U.S. populations may be limited. Diverse populations with varying genetic backgrounds, dietary habits, and healthcare access may present distinct cardiometabolic trajectories( 34 – 39 ). Additionally, the FI employed in the current study, while based on a validated deficit accumulation model, was constrained by variable availability across datasets. Variability in the operationalization of frailty may introduce measurement bias or reduce comparability. Standardized and harmonized assessments of frailty, including dynamic frailty trajectories, should be prioritized in future analyses. Lastly, while our findings support the predictive value of the TyGFI as a composite indicator, its integration into clinical practice requires further validation. Future research should adopt longitudinal and interventional designs, incorporate dynamic modeling of TyGFI trajectories, and evaluate the clinical utility of TyGFI in risk prediction tools. In summary, despite certain limitations, this study highlights the TyGFI as a novel and clinically accessible composite marker that integrates metabolic dysfunction and frailty. It demonstrates strong potential for improving cardiovascular and stroke risk stratification, particularly in aging populations. 5. Conclusion This study identified a strong and independent association between higher TyGFI levels and increased risks of cardiovascular disease and stroke in both CHARLS and NHANES cohorts. Participants with elevated TyGFI showed worse metabolic profiles and a higher burden of chronic conditions. The associations were dose-dependent and remained robust after adjusting for multiple confounders. Subgroup and spline analyses confirmed the consistency and nonlinearity of these relationships. These findings suggest that TyGFI is a practical and integrative marker for identifying individuals at higher cardiometabolic risk, especially in aging populations. Declarations Acknowledgments The authors would like to express their sincere gratitude to the original data collectors, depositors, copyright holders, and funding bodies of the CHARLS for making these invaluable data publicly available. We are especially grateful to the NHANES team for their rigorous data collection and management efforts, which provided the empirical foundation for this study. Our heartfelt appreciation extends to the NHANES participants whose time and contributions made this research possible. We further acknowledge the valuable insights and critical feedback offered by our colleagues during the development of this manuscript. Finally, we thank our home institution for its administrative and technical support throughout the course of this work. Author contributions Yi-Chang Zhao, Shi-Qi Wu, Jia-Kai Li, Zhi-Hua Sun, Bi-Kui Zhang, Rao Fu, and Miao Yan took responsibility for the integrity and accuracy of the data analysis. The concept and design of the study were developed by Yi-Chang Zhao, Bi-Kui Zhang, Rao Fu, and Miao Yan. Data acquisition, analysis, or interpretation were performed by Yi-Chang Zhao, Shi-Qi Wu, Bi-Kui Zhang, Rao Fu, and Miao Yan. Yi-Chang Zhao and Miao Yan drafted the manuscript, while Rao Fu, and Miao Yan provided critical revisions for intellectual content. Statistical analysis was conducted by Yi-Chang Zhao and Shi-Qi Wu, and administrative, technical, or material support was provided by Shi-Qi Wu, and Yi-Chang Zhao. Supervision of the study was overseen by Bi-Kui Zhang and Miao Yan. Sensitivity and risk-of-bias assessments were conducted by Yi-Chang Zhao, and study selection and data collection were carried out by Yi-Chang Zhao, Rao Fu, and Miao Yan. All authors reviewed and approved the final manuscript, ensuring its scientific rigor and accuracy. Funding This study was supported by the Teaching Reform Program for Graduate Education at Central South University [Grant No. 2023JGB039], the Hunan Provincial Degree and Graduate Education Reform Project [Grant No. 2023JGYB041], and the Hunan Provincial Health High-Level Talent Scientific Research Project [Grant No. R2023061]. Additional funding was provided by the Research Project established by the Chinese Pharmaceutical Association Hospital Pharmacy Department [Grant No. CPA-Z05-ZC-2024002], and the Postgraduate Innovative Project of Central South University [Grant No. 2024XQLH030]. Data availability Data supporting the findings of this study are publicly available from the official websites of the CHARLS (http://charls.pku.edu.cn) and the NHANES (https://www.cdc.gov/nchs/nhanes/index.htm). Access to both datasets is granted for research purposes upon request or registration, in accordance with their respective data use policies. Ethics approval and consent to participate The China Health and Retirement Longitudinal Study was approved by the Ethics Review Committee of Peking University. The National Health and Nutrition Examination Survey were approved by the National Center for Health Statistics ethics review board. Written informed consent was obtained from all participants. Informed consent was obtained from each subject in these two cohorts. Consent for publication This manuscript is not currently under consideration for publication elsewhere, and the work reported will not be submitted for publication elsewhere until a final decision has been made as to its acceptability by the journal. Competing interest All authors declare no conflict of interest. Author details a Department of Pharmacy, the Second Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China 410011. b International Research Center for Precision Medicine, Transformative Technology and Software Services, Hunan, China. c National Clinical Research Center for Metabolic Diseases, Changsha, Hunan, P.R. China 410011. d China Pharmaceutical University, Nanjing, Jiangsu, P.R. China 210009. Address: Department of Clinical Pharmacy, the Second Xiangya Hospital of Central South University, Changsha 410010, Hunan Province, China. E-mail: [email protected] Tel: 086-0731-85292098 # The two authors contributed equally to this work and share first authorship References Vogel B, Acevedo M, Appelman Y, et al. The Lancet women and cardiovascular disease Commission: reducing the global burden by 2030. Lancet. 2021;397(10292):2385–438. 10.1016/s0140-6736(21)00684-x . Feigin VL, Brainin M, Norrving B, et al. World Stroke Organization (WSO): Global Stroke Fact Sheet 2022. Int J Stroke. 2022;17(1):18–29. 10.1177/17474930211065917 . Tu WJ, Wang LD. China stroke surveillance report 2021. 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Interacting and joint effects of triglyceride-glucose index (TyG) and body mass index on stroke risk and the mediating role of TyG in middle-aged and older Chinese adults: a nationwide prospective cohort study. Cardiovasc Diabetol. 2024;23(1):30. 10.1186/s12933-024-02122-4 . Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008;8:24. 10.1186/1471-2318-8-24 . Cai W, Xu J, Wu X, et al. Association between triglyceride-glucose index and all-cause mortality in critically ill patients with ischemic stroke: analysis of the MIMIC-IV database. Cardiovasc Diabetol. 2023;22(1):138. 10.1186/s12933-023-01864-x . Che B, Zhong C, Zhang R, et al. Triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio as potential cardiovascular disease risk factors: an analysis of UK biobank data. Cardiovasc Diabetol. 2023;22(1):34. 10.1186/s12933-023-01762-2 . Chen T, Qian Y, Deng X. Triglyceride glucose index is a significant predictor of severe disturbance of consciousness and all-cause mortality in critical cerebrovascular disease patients. Cardiovasc Diabetol. 2023;22(1):156. 10.1186/s12933-023-01893-6 . Cui C, Qi Y, Song J, et al. Comparison of triglyceride glucose index and modified triglyceride glucose indices in prediction of cardiovascular diseases in middle aged and older Chinese adults. Cardiovasc Diabetol. 2024;23(1):185. 10.1186/s12933-024-02278-z . Ding X, Wang X, Wu J, Zhang M, Cui M. Triglyceride-glucose index and the incidence of atherosclerotic cardiovascular diseases: a meta-analysis of cohort studies. Cardiovasc Diabetol. 2021;20(1):76. 10.1186/s12933-021-01268-9 . Cui C, Liu L, Zhang T, et al. Triglyceride-glucose index, renal function and cardiovascular disease: a national cohort study. Cardiovasc Diabetol. 2023;22(1):325. 10.1186/s12933-023-02055-4 . Yang Y, Huang X, Wang Y, et al. The impact of triglyceride-glucose index on ischemic stroke: a systematic review and meta-analysis. Cardiovasc Diabetol. 2023;22(1):2. 10.1186/s12933-022-01732-0 . Wu Y, Yang Y, Zhang J, Liu S, Zhuang W. The change of triglyceride-glucose index may predict incidence of stroke in the general population over 45 years old. Cardiovasc Diabetol. 2023;22(1):132. 10.1186/s12933-023-01870-z . Huang Z, Ding X, Yue Q, et al. Triglyceride-glucose index trajectory and stroke incidence in patients with hypertension: a prospective cohort study. Cardiovasc Diabetol. 2022;21(1):141. 10.1186/s12933-022-01577-7 . Cui C, Liu L, Qi Y, et al. Joint association of TyG index and high sensitivity C-reactive protein with cardiovascular disease: a national cohort study. Cardiovasc Diabetol. 2024;23(1):156. 10.1186/s12933-024-02244-9 . Qu L, Fang S, Lan Z, et al. Association between atherogenic index of plasma and new-onset stroke in individuals with different glucose metabolism status: insights from CHARLS. Cardiovasc Diabetol. 2024;23(1):215. 10.1186/s12933-024-02314-y . Burton JK, Stewart J, Blair M, et al. Prevalence and implications of frailty in acute stroke: systematic review & meta-analysis. Age Ageing. 2022;51(3). 10.1093/ageing/afac064 . Zhu J, Zhou D, Wang J, et al. Frailty and cardiometabolic diseases: a bidirectional Mendelian randomisation study. Age Ageing. 2022;51(11). 10.1093/ageing/afac256 . Ren Q, Huang Y, Liu Q, Chu T, Li G, Wu Z. Association between triglyceride glucose-waist height ratio index and cardiovascular disease in middle-aged and older Chinese individuals: a nationwide cohort study. Cardiovasc Diabetol. 2024;23(1):247. 10.1186/s12933-024-02336-6 . Jiang Y, Shen J, Chen P, et al. Association of triglyceride glucose index with stroke: from two large cohort studies and Mendelian randomization analysis. Int J Surg. 2024;110(9):5409–16. 10.1097/js9.0000000000001795 . Aune SK, Helseth R, Kalstad AA, et al. Links Between Adipose Tissue Gene Expression of Gut Leakage Markers, Circulating Levels, Anthropometrics, and Diet in Patients with Coronary Artery Disease. Diabetes Metab Syndr Obes. 2024;17:2177–90. 10.2147/dmso.S438818 . Bermingham KM, Smith HA, Gonzalez JT, et al. Glycaemic variability, assessed with continuous glucose monitors, is associated with diet, lifestyle and health in people without diabetes. Res Sq. 2023. 10.21203/rs.3.rs-3469475/v1 . Biemans Y, Bach D, Behrouzi P, et al. Identifying the relation between food groups and biological ageing: a data-driven approach. Age Ageing. 2024;53(Suppl 2):ii20–9. 10.1093/ageing/afae038 . Dashti HS, Chen A, Daghlas I, Saxena R. Morning diurnal preference and food intake: a Mendelian randomization study. Am J Clin Nutr. 2020;112(5):1348–57. 10.1093/ajcn/nqaa219 . Li G, Zhong L, Han L, et al. Genetic variations in adiponectin levels and dietary patterns on metabolic health among children with normal weight versus obesity: the BCAMS study. Int J Obes (Lond). 2022;46(2):325–32. 10.1038/s41366-021-01004-z . Hu MJ, Tan JS, Gao XJ, Yang JG, Yang YJ. Effect of Cheese Intake on Cardiovascular Diseases and Cardiovascular Biomarkers. Nutrients. 2022;14(14). 10.3390/nu14142936 . Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6835376","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470611395,"identity":"83f9f570-feef-4108-a4a0-d00ba449023c","order_by":0,"name":"Yi-Chang Zhao","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yi-Chang","middleName":"","lastName":"Zhao","suffix":""},{"id":470611396,"identity":"3b764a56-5997-4874-a39e-8a8de1400bbc","order_by":1,"name":"Shi-Qi Wu","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Shi-Qi","middleName":"","lastName":"Wu","suffix":""},{"id":470611399,"identity":"10a2edef-6363-41d3-8341-b413989e5144","order_by":2,"name":"Jia-Kai Li","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jia-Kai","middleName":"","lastName":"Li","suffix":""},{"id":470611400,"identity":"8fd93a79-7d8f-47b7-88e3-b379d27c8797","order_by":3,"name":"Zhi-Hua Sun","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Zhi-Hua","middleName":"","lastName":"Sun","suffix":""},{"id":470611401,"identity":"095f1911-a1e3-4b63-b410-7d20fd3c74be","order_by":4,"name":"Bi-Kui Zhang","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Bi-Kui","middleName":"","lastName":"Zhang","suffix":""},{"id":470611403,"identity":"694f4b48-78cc-4b36-9b44-1dd05a4d541c","order_by":5,"name":"Rao Fu","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Rao","middleName":"","lastName":"Fu","suffix":""},{"id":470611404,"identity":"0733413a-5d19-40aa-a4c3-33a4aa7b5094","order_by":6,"name":"Miao Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYLCCBAYGOQiLjQQtxiRqAYLEBqK1GBw/e3TDgxqb9P7+MwYMH8oOM/DPbiCg5Uxe2o2EY2m5M27kGDDOOHeYQeLOAQJaDuSY3UhgO5y7QYLHgJm37TCDgUQCAS3n3wC1/DucbsB/xoD5L1FabgBtSWw7nGDAkGPAzEiMFskbQFsS+9IMZ9xIKzjYcy6dR+IGAS1853PMbv74ZiPP339444MfZdZy/DMIaFE4gMQBsXnwqwcC+QaCSkbBKBgFo2DEAwBJ8kZ1La2uRwAAAABJRU5ErkJggg==","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":true,"prefix":"","firstName":"Miao","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2025-06-06 08:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6835376/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6835376/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12933-025-02880-9","type":"published","date":"2025-08-04T15:56:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84669602,"identity":"1009a54d-1e37-4697-a33c-ae921d663330","added_by":"auto","created_at":"2025-06-16 06:29:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29872,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow Diagram of Participant Selection from the CHARLS and NHANES Cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLegend: This figure illustrates the stepwise selection of study participants from the 2011 wave of the CHARLS and from the 2001–2011 cycles of the U.S. NHANES. Participants were excluded if they were younger than 45 years, had a history of stroke or CVD, or had missing values for serum triglyceride or glucose. The final eligible samples in each cohort were then divided into four groups (Quartiles 1–4) based on the distribution of the variable of interest (e.g., TyG index). The numbers shown under each box represent the sample size remaining at each stage of inclusion and exclusion.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6835376/v1/0b6c5195f65e45cfceb32d47.png"},{"id":84670375,"identity":"374d9115-af2a-4e1e-9ef4-30965ec73500","added_by":"auto","created_at":"2025-06-16 06:37:17","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":223885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariable Logistic Regression Results and Restricted Cubic Spline Curves of the Association between TyG-FI and CVD Risk in the CHARLS and NHANES Cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLegend:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) A forest plot from the CHARLS cohort showing the results of multivariable logistic regression analyses for CVD as the outcome. It displays the adjusted OR, 95% CI, and p-values across four different models.\u003c/p\u003e\n\u003cp\u003e(B) A forest plot from the NHANES cohort showing the results of multivariable logistic regression analyses for CVD as the outcome. It similarly displays the OR, 95% CI, and p-values across four different models.\u003c/p\u003e\n\u003cp\u003e(C) A dose–response curve from the CHARLS cohort based on RCS analysis, illustrating the relationship between TyG-FI and CVD risk. The shaded area represents the 95% confidence interval.\u003c/p\u003e\n\u003cp\u003e(D) A dose–response curve from the NHANES cohort based on RCS analysis, illustrating the relationship between TyG-FI and CVD risk. The shaded area represents the 95% confidence interval.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6835376/v1/d221e622d876118330a24fb5.jpeg"},{"id":84669603,"identity":"35524ad5-1a40-45bd-9636-c3a533dfce17","added_by":"auto","created_at":"2025-06-16 06:29:17","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":177950,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariable Logistic Regression Results and Restricted Cubic Spline Curves of the Association between TyG-FI and Stroke Risk in the CHARLS and NHANES Cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLegend:\u003c/p\u003e\n\u003cp\u003e(A) A forest plot from the CHARLS cohort showing the results of multivariable logistic regression analyses for stroke as the outcome. It displays the adjusted OR, 95% CI, and p-values across four different models.\u003c/p\u003e\n\u003cp\u003e(B) A forest plot from the NHANES cohort showing the results of multivariable logistic regression analyses for stroke as the outcome. It similarly displays the OR, 95% CI, and p-values across four different models.\u003c/p\u003e\n\u003cp\u003e(C) A dose–response curve from the CHARLS cohort based on RCS analysis, illustrating the relationship between TyG-FI and stroke risk. The shaded area represents the 95% confidence interval.\u003c/p\u003e\n\u003cp\u003e(D) A dose–response curve from the NHANES cohort based on RCS analysis, illustrating the relationship between TyG-FI and stroke risk. The shaded area represents the 95% CI.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6835376/v1/dcfb2022b7cd9f83575b5b1a.jpeg"},{"id":88814042,"identity":"6909409a-77a0-4fcb-8509-58236f4858c2","added_by":"auto","created_at":"2025-08-11 16:03:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1053069,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6835376/v1/db5ae204-7aa3-43d8-a3fe-20ff0a41474d.pdf"},{"id":84669599,"identity":"3f9b03ba-4c96-4d07-876f-cd46765d5ceb","added_by":"auto","created_at":"2025-06-16 06:29:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":36349,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6835376/v1/604db87a6af0f0a15a1e246a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of the Combined TyG and FI Index for Cardiovascular Disease and Stroke in two prospective cohorts","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) and stroke remain the leading causes of mortality and disability worldwide, posing a substantial burden on global public health. Over 17 million people die each year from CVD, accounting for nearly one-third of all global deaths, with stroke ranking as the second leading cause of death and exhibiting a concerning shift toward younger populations(1, 2). In China, stroke has become the leading cause of death and disability, with approximately 17.8 million prevalent cases, 3.4 million new cases annually, and 2.3 million stroke-related deaths reported in 2020(3, 4). As the aging population grows, the incidence and mortality rates of CVD and stroke continue to rise among older adults, underscoring the urgent need for more refined risk assessment tools to enable early prevention and intervention(5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe triglyceride-glucose (TyG) index has emerged in recent years as a reliable and accessible surrogate marker of insulin resistance. A growing body of literature has confirmed its predictive value for metabolic disorders and atherosclerotic diseases, including diabetes, coronary artery disease, heart failure, and stroke(6-9). In addition to these primary outcomes, elevated TyG levels have also been associated with complex vascular outcomes such as carotid plaque progression, cardiovascular mortality, and post-stroke depression, expanding its utility in clinical risk stratification(10, 11). In parallel, frailty index (FI)—a multidimensional clinical syndrome characterized by reduced physiological reserve and increased vulnerability to stressors—has been independently linked to a range of adverse outcomes, including CVD, stroke, falls, cognitive decline, and all-cause mortality(12-14). The high prevalence of frailty in older adults and its predictive relevance for poor health outcomes make it an essential component of geriatric risk models. Recent evidence has shown that worsening frailty is associated with increased CVD risk, whereas frailty remission may correspond with lower event rates(15).\u003c/p\u003e\n\u003cp\u003eDespite the established individual roles of TyG and frailty in predicting cardiovascular risk, they have largely been assessed in isolation. Few studies have explored their combined effect, even though metabolic dysfunction and physiological vulnerability frequently coexist in older adults. These coexisting conditions may act synergistically through mechanisms such as chronic inflammation, oxidative stress, and endothelial dysfunction to promote vascular damage and accelerate atherosclerosis(16, 17). Moreover, traditional risk prediction models—such as the Framingham Risk Score—do not adequately incorporate aging-related variables such as frailty or low-grade inflammation, limiting their accuracy in elderly populations.\u003c/p\u003e\n\u003cp\u003eTo address this gap, the present study introduces a novel composite risk indicator—the TyG-Frailty Index (TyGFI)—designed to capture the dual burden of metabolic stress and physiological vulnerability. Using data from two nationally representative cohorts, the China Health and Retirement Longitudinal Study (CHARLS) and the U.S. National Health and Nutrition Examination Survey (NHANES), we systematically examine the associations between TyGFI and the risks of CVD and stroke. Through multivariable logistic regression, restricted cubic spline (RCS) modeling, and stratified subgroup analyses, this study aims to evaluate the independent and joint predictive value of TyGFI. Our findings have the potential to inform a more nuanced and integrative approach to cardiovascular risk assessment, particularly among older adults with high multimorbidity burden.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eStudy Design Overview\u003c/p\u003e\n\u003cp\u003eThis study conducted a comprehensive analysis of the association between the combined TyG and FI and the incidence of CVD and stroke, adjusting for multiple potential confounders. Data were obtained from two nationally cohorts: the NHANES and the CHARLS. The findings were rigorously validated through sensitivity analyses. Detailed procedures for data extraction from the two cohorts are depicted in the flowcharts (Figure 1). The CHARLS cohort initially comprised 17,596 participants from the 2011 survey. The final analytical sample included 5,448 participants, stratified into four quartiles (n=1,358\u0026ndash;1,369). Meanwhile, the NHANES cohort yielded a final sample of 1,139 participants, also divided into quartiles (n=282\u0026ndash;287). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo Extensive Observational Cohort Studies\u003c/p\u003e\n\u003cp\u003eNHANES Database Cohort: NHANES is a comprehensive health examination and nutritional assessment survey of adults and children conducted in the United States. The database includes demographic information, dietary habits, laboratory tests, physical examinations, and questionnaire responses collected between 2001 and 2023. The NHANES study protocol received approval from the Ethics Review Board of the National Center for Health Statistics, and written informed consent was obtained from all participants prior to data collection.\u003c/p\u003e\n\u003cp\u003eCHARLS Database Cohort: Data were also sourced from CHARLS, a nationally representative longitudinal survey designed to capture health and economic data among China\u0026apos;s elderly population. CHARLS covers demographic information, physical health assessments, cognitive tests, biomarkers, and socioeconomic indicators. Ethical approval for CHARLS was provided by the Institutional Review Board at Peking University, and informed consent was obtained from all study participants.\u003c/p\u003e\n\u003cp\u003eData collection and definitions\u003c/p\u003e\n\u003cp\u003eThe CHARLS dataset encompassed data collected between 2011 and 2015, while the NHANES dataset covered the period from 2001 to 2023, with baseline data specifically restricted to 2011 or earlier. Collected variables included demographic characteristics such as age, sex, and education level. Clinical measurements recorded included systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), glucose, hemoglobin A1c (HbA1c), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDLC), and low-density lipoprotein cholesterol (LDLC). Lifestyle factors such as smoking and drinking status were documented, alongside medical histories of dyslipidemia, hypertension, and diabetes mellitus (DM). The TyG index was calculated using the formula: Ln [triglycerides (mg/dl) \u0026times; glucose (mg/dl)/2]. Frailty was measured in relation to the accumulation of deficits using FI(18). The combined TyG and FI(18) was computed using: TyGFI = TyG \u0026times; FI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were conducted to summarize demographic and clinical characteristics. Continuous variables were summarized as means \u0026plusmn; standard deviations. Categorical variables were summarized as frequencies and percentages. Logistic regression analyses were employed to evaluate associations between the combined TyGFI and outcomes CVD and stroke with adjustments for potential confounders. Both continuous TyGFI and categorical TyGFI (median-based qua。、GG？Rtiles) were analyzed, with odds ratios (ORs), 95% confidence intervals (CIs), and p-values reported. Restricted cubic spline (RCS) analysis was applied to evaluate nonlinear associations, visualized through plots indicating the probability of CVD and stroke. Stratified subgroup analyses were performed based on variables such as sex, smoking status, drinking status, hypertension, DM, dyslipidemia, and education. Results were visualized using forest plots. The study strictly adhered to STROBE guidelines and accounted for NHANES survey design and sampling weights.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eBaseline characteristics of the study population\u003c/p\u003e\n\u003cp\u003eAccording to the inclusion and exclusion criteria, a total of 5448 participants from the CHARLS cohort (female: 53.71%, mean age: 58.65 years) and 1139 from \u0026nbsp;the NHANES cohort (female: 52.74%, mean age: 60.96 years) were included in the baseline analyses. Detailed participant characteristics are summarized in Table 1. Participants were categorized into quartiles (Q1\u0026ndash;Q4) based on their TyGFI levels. Across both cohorts, individuals in higher TyGFI quartiles tended to be older and exhibited more adverse metabolic profiles. In CHARLS, average age increased from 56.4 in Q1 to 61.9 years in Q4, and in NHANES from 59.1 to 62.8 years. BMI followed a similar trend, rising from 23.2 to 23.9 kg/m\u0026sup2; in CHARLS and from 27.6 to 31.7 kg/m\u0026sup2; in NHANES. Metabolic biomarkers also showed significant changes. Fasting glucose increased from 104.7 to 116.7 mg/dL in CHARLS and from 100.9 to 128.2 mg/dL in NHANES. HbA1c rose from 5.1% to 5.4% in CHARLS and from 5.5% to 6.4% in NHANES. In both cohorts, TG, TC, and LDLC increased across TyGFI quartiles, while HDLC declined. Prevalence of chronic conditions also increased with TyGFI. In CHARLS, hypertension rose from 21% in Q1 to 56% in Q4, dyslipidemia from 34% to 46%, and DM from 8% to 22%. Similar increases were observed in NHANES, with DM rising from 25% to 64%. Smoking prevalence and lower educational attainment were more common in higher TyGFI groups. Notably, drinking frequency declined with TyGFI in CHARLS but showed no consistent pattern in NHANES.\u003c/p\u003e\n\u003cp\u003eAssociation between TyGFI index and cardiovascular disease risk\u003c/p\u003e\n\u003cp\u003eThe association between the TyGFI and the risk of CVD was evaluated in both the CHARLS and NHANES cohorts using logistic regression models with increasing levels of covariate adjustment (Table 2). In the CHARLS cohort, a strong and graded association was observed between TyGFI quartiles and CVD risk. Compared to participants in the lowest quartile (Q1), those in Q2, Q3, and Q4 showed significantly elevated odds of cardiovascular events. In the fully adjusted model (Model 3), the odds ratios (ORs) for CVD were 3.55 (95% CI: 2.21\u0026ndash;5.70) in Q2, 7.77 (95% CI: 4.96\u0026ndash;12.18) in Q3, and 15.09 (95% CI: 9.65\u0026ndash;23.60) in Q4 (p for trend \u0026lt;0.0001), which were consistent across all models. In the NHANES cohort, similar trends were observed, though with slightly attenuated effect sizes. In Model 3, compared with Q2 (reference), Q3 had an OR of 3.41 (95% CI: 1.36\u0026ndash;8.56) and Q4 had an OR of 4.98 (95% CI: 2.04\u0026ndash;12.19), both statistically significant. The p for trend remained highly significant (p \u0026lt;0.001) in all models except Model 2 (p = 0.11), which still showed elevated point estimates. When TyGFI was analyzed as a continuous variable using its median value, the association with CVD remained robust across all models in both cohorts. After adjusting for age, sex, lifestyle factors, blood pressure, glycemic and lipid parameters, and socioeconomic variables (Model 3), TyGFI remained independently associated with increased cardiovascular risk. These findings from two nationally representative cohorts underscore a consistent and independent relationship between higher TyGFI and elevated risk of CVD, reinforcing the potential clinical utility of TyGFI as a composite marker for cardiovascular risk stratification as also shown in Table 2.\u003c/p\u003e\n\u003cp\u003eSubgroup analysis for TyGFI index and cardiovascular disease risk\u003c/p\u003e\n\u003cp\u003eSubgroup analyses were conducted for both the CHARLS and NHANES cohorts, focusing on the association between TyGFI and CVD risk. The results from the CHARLS cohort (Fig. 2A) demonstrated a significant and consistent increase in CVD risk across TyGFI quartiles, with stronger associations observed among males, smokers, drinkers, and individuals with hypertension, diabetes mellitus (DM), and dyslipidemia. Participants with lower education levels also exhibited higher risk estimates. The RCS analysis (Fig. 2C) indicated a nonlinear positive association between TyGFI and CVD risk, further reinforcing the findings from the categorical quartile analysis.\u003c/p\u003e\n\u003cp\u003eSimilarly, in the NHANES cohort (Fig. 2B), a stepwise increase in CVD risk was observed with higher TyGFI quartiles. The subgroup analysis revealed particularly strong associations among smokers, drinkers, and individuals with hypertension, DM, and dyslipidemia. Educational disparities were also noted, with lower education levels correlating with elevated CVD risk. RCS analysis (Fig. 2D) confirmed the nonlinear relationship between TyGFI and CVD, demonstrating a consistent trend across different adjustment models.\u003c/p\u003e\n\u003cp\u003eAssociation between TyGFI index and stroke risk\u003c/p\u003e\n\u003cp\u003eTable 3 presents the associations between the TyGFI and stroke across the CHARLS and NHANES cohorts. In both cohorts, higher TyGFI levels were significantly associated with an increased risk of stroke. In the CHARLS cohort, each unit increase in TyGFI was associated with a markedly elevated risk of stroke across all models. In the fully adjusted Model 3, the hazard ratio (HR) was 8.99 (95% CI: 5.53\u0026ndash;14.62, p \u0026lt; 0.0001). When TyGFI was analyzed by quartiles, a clear dose-response relationship was observed. Compared to the reference group (Q1), individuals in Q3 and Q4 had significantly higher risks of stroke in all models. In Model 3, the OR were 8.22 (95% CI: 2.45\u0026ndash;27.58, p \u0026lt; 0.001) for Q3 and 21.12 (95% CI: 6.44\u0026ndash;69.23, p \u0026lt; 0.0001) for Q4, with a significant trend across quartiles (p for trend \u0026lt; 0.0001). Similar patterns were observed in the NHANES cohort. Although the associations were somewhat attenuated compared to CHARLS, the trend remained significant. In the fully adjusted Model 3, the OR for Q3 and Q4 were 6.36 (95% CI: 1.44\u0026ndash;28.00, p = 0.02) and 12.98 (95% CI: 2.58\u0026ndash;65.17, p = 0.004), respectively, with a consistent trend across quartiles (p for trend \u0026lt; 0.001). The OR for Q2 in both cohorts did not reach statistical significance.\u003c/p\u003e\n\u003cp\u003eSubgroup analysis for TyGFI index and stroke risk\u003c/p\u003e\n\u003cp\u003eSubgroup analyses were also performed to assess the relationship between TyGFI and stroke risk across different demographic and clinical subgroups within the CHARLS and NHANES cohorts. In the CHARLS cohort (Fig. 3A), a clear trend emerged, showing a significant escalation in stroke risk as TyGFI quartiles increased. Compared with participants in the lowest quartile, those in Q4 exhibited the highest stroke risk, particularly among males, individuals who consumed alcohol, and those diagnosed with dyslipidemia. Males in Q4 had markedly greater odds of stroke (OR 42.47, 95% CI: 9.067\u0026ndash;157.63, p\u0026lt;0.001), as did drinkers (OR 18.48, 95% CI: 3.619\u0026ndash;347.36, p=0.005) and participants with dyslipidemia (OR 23.06, 95% CI: 4.918\u0026ndash;411.43, p=0.002). The RCS analysis (Fig. 3C) further illustrated a nonlinear association, demonstrating an accelerating increase in stroke probability at higher TyGFI levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFindings from the NHANES cohort (Fig. 3B) were consistent, revealing a stepwise elevation in stroke risk with increasing TyGFI quartiles. The association was particularly pronounced among those with hypertension, dyslipidemia, and lower educational attainment. Participants with hypertension in Q4 experienced a significantly heightened risk (OR 11.34, 95% CI: 2.526\u0026ndash;79.023, p=0.004), as did those with dyslipidemia (OR 14.98, 95% CI: 5.375\u0026ndash;62.349, p\u0026lt;0.001). Moreover, individuals with only primary school education demonstrated a stronger association with stroke risk (OR 14.25, 95% CI: 0.855\u0026ndash;259.03, p=0.010). The RCS analysis (Fig. 3D) corroborated these findings, confirming a nonlinear dose-response relationship between TyGFI and stroke risk, with a sharp increase in probability observed at higher TyGFI values.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis investigation elucidates a robust and independent relationship between the combined TyG and FI indices and the risk of both CVD and stroke. Utilizing two large, nationally representative cohorts\u0026mdash;CHARLS and NHANES\u0026mdash;our analysis demonstrates that elevated TyGFI levels are consistently associated with increased odds of adverse cardiovascular outcomes. Furthermore, this association exhibits a clear dose-response pattern, as participants within the highest TyGFI quartiles experienced significantly greater risk elevations. Importantly, these findings remained stable after rigorous adjustment for a wide array of demographic, clinical, and lifestyle confounders. The observed associations were further corroborated through subgroup analyses, which revealed consistent effects across diverse demographic and clinical strata. Collectively, these results emphasize the potential clinical relevance of the TyGFI as an integrative risk marker for cardiovascular disease prevention and management. The association between higher TyGFI levels and increased risks of CVD and stroke likely reflects the combined effects of metabolic dysfunction and frailty on vascular health. Individuals in higher TyGFI quartiles had worse metabolic profiles, including higher BMI, glucose, HbA1c, and unfavorable lipid levels\u0026mdash;all known risk factors for atherosclerosis. The clear dose-response pattern in both CHARLS and NHANES supports TyGFI as a reliable tool for cardiovascular risk stratification, even after adjusting for various confounders. Subgroup analyses further show that TyGFI can amplify risk in the presence of traditional factors like hypertension, diabetes, and dyslipidemia. The consistent findings across two large and diverse cohorts highlight TyGFI's broad applicability and potential clinical value.\u003c/p\u003e \u003cp\u003eExtensive research has established the TyG as a reliable surrogate marker for insulin resistance and a significant predictor of adverse cardiovascular and cerebrovascular outcomes. Prior studies have consistently demonstrated that elevated TyG levels are associated with increased risks of atherosclerotic cardiovascular disease (ASCVD), stroke, and mortality(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Ding et al. (2021) confirmed that individuals with higher TyG levels were more likely to develop cardiovascular events, even in the absence of baseline CVD(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Cui et al. further highlighted the prognostic value of TyG, particularly in populations with compromised renal function(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In addition, Chen et al. reported a strong association between TyG and both all-cause and cardiovascular mortality(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In the domain of stroke research, elevated TyG has also been linked to increased incidence, recurrence, and poor prognosis. Yang et al. identified a significant association between higher TyG levels and the risk of ischemic stroke and its recurrence(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), while Cai et al. demonstrated that TyG was independently associated with in-hospital and ICU mortality in patients with critical stroke(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Moreover, longitudinal analyses conducted by Wu et al. and Huang et al. emphasized that persistent or increasing TyG trajectories over time were significantly correlated with elevated stroke risk(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). These findings underscore the importance of TyG as a long-term indicator of metabolic risk. Emerging evidence has also begun to clarify the potential mechanisms behind these associations. Huo et al. showed that TyG mediated a substantial proportion of the relationship between body mass index and stroke, suggesting its involvement in key pathophysiological pathways linking obesity to vascular injury(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough previous studies have clearly established the independent role of TyG in predicting cardiovascular and cerebrovascular events, the present study contributes a novel perspective by incorporating frailty\u0026mdash;a frequently overlooked, yet clinically relevant, determinant of cardiovascular risk. Recent findings by He et al. demonstrated that progression in frailty status significantly increased the risk of cardiovascular events, independent of traditional metabolic risk factors(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, few investigations have attempted to combine metabolic and functional indicators into a single index.\u003c/p\u003e \u003cp\u003eOverall, this study exhibits multiple methodological and conceptual strengths that reinforce the robustness, generalizability, and clinical relevance of its findings. Notably, the utilization of two nationally representative and demographically distinct cohorts\u0026mdash;CHARLS from China and NHANES from the United States\u0026mdash;ensures broad external validity across populations with diverse sociocultural, genetic, and healthcare backgrounds. The consistency of results across these cohorts enhances the credibility of the observed associations and addresses an important limitation in prior cardiovascular research(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, the study advances the field by proposing a novel composite risk indicator\u0026mdash;TyGFI\u0026mdash;that integrates the well-validated TyG, a surrogate marker of insulin resistance, with FI, a widely accepted measure of cumulative physiological deficits. While both TyG and FI have been independently associated with cardiovascular and cerebrovascular outcomes(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), their combination into a single metric represents a conceptual innovation. This dual-domain approach captures both metabolic and functional deterioration, enabling a more holistic and sensitive assessment of cardiovascular risk, particularly among aging individuals. In addition, the analytical framework is characterized by rigorous adjustment for a comprehensive set of covariates, including demographic, socioeconomic, behavioral, and clinical factors. The application of multivariable logistic regression, restricted cubic spline modeling, and stratified subgroup analyses ensures statistical robustness and allows for the detection of both linear and nonlinear relationships. Besides, the study also contributes to the literature by empirically demonstrating the added predictive value of TyGFI over its individual components. In doing so, it addresses a critical gap identified in previous studies that evaluated isolated metabolic indicators, such as TyG-BMI and TyG-WHtR, without accounting for functional health status(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The integrative nature of TyGFI offers a more refined stratification tool for identifying individuals at elevated risk of cardiovascular and cerebrovascular events. Therefore, these strengths underscore the originality and applicability of TyGFI as a multidimensional risk marker, offering significant potential for incorporation into precision prevention strategies for cardiovascular and cerebrovascular disease.\u003c/p\u003e \u003cp\u003eHowever, despite the methodological rigor and cross-cohort validation, several limitations of this study should be acknowledged, particularly in the context of temporal dynamics and evolving risk factors. First, the analysis was based on baseline measurements of the TyG and FI, which are both time-sensitive indicators. However, growing evidence emphasizes the importance of longitudinal changes and cumulative exposure to these markers. For instance, Wu et al. and Huang et al. demonstrated that persistent elevation or upward trajectories in TyG over time were significantly associated with increased stroke risk in middle-aged and older adults(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Similarly, He et al. showed that progression in frailty status over time was closely linked with elevated CVD incidence(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Second, although the study employed multivariable adjustment to control for confounding factors, its observational nature precludes definitive causal inference. While the associations identified are robust and consistent across two nationally representative cohorts, unmeasured confounding remains a possibility. Incorporating analytical approaches such as Mendelian randomization or instrumental variable analysis could strengthen causal interpretations, as illustrated by Jiang et al.(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Third, although CHARLS and NHANES represent different sociocultural and healthcare contexts, generalizability beyond Chinese and U.S. populations may be limited. Diverse populations with varying genetic backgrounds, dietary habits, and healthcare access may present distinct cardiometabolic trajectories(\u003cspan additionalcitationids=\"CR35 CR36 CR37 CR38\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Additionally, the FI employed in the current study, while based on a validated deficit accumulation model, was constrained by variable availability across datasets. Variability in the operationalization of frailty may introduce measurement bias or reduce comparability. Standardized and harmonized assessments of frailty, including dynamic frailty trajectories, should be prioritized in future analyses. Lastly, while our findings support the predictive value of the TyGFI as a composite indicator, its integration into clinical practice requires further validation. Future research should adopt longitudinal and interventional designs, incorporate dynamic modeling of TyGFI trajectories, and evaluate the clinical utility of TyGFI in risk prediction tools.\u003c/p\u003e \u003cp\u003eIn summary, despite certain limitations, this study highlights the TyGFI as a novel and clinically accessible composite marker that integrates metabolic dysfunction and frailty. It demonstrates strong potential for improving cardiovascular and stroke risk stratification, particularly in aging populations.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study identified a strong and independent association between higher TyGFI levels and increased risks of cardiovascular disease and stroke in both CHARLS and NHANES cohorts. Participants with elevated TyGFI showed worse metabolic profiles and a higher burden of chronic conditions. The associations were dose-dependent and remained robust after adjusting for multiple confounders. Subgroup and spline analyses confirmed the consistency and nonlinearity of these relationships. These findings suggest that TyGFI is a practical and integrative marker for identifying individuals at higher cardiometabolic risk, especially in aging populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to the original data collectors, depositors, copyright holders, and funding bodies of the CHARLS for making these invaluable data publicly available. We are especially grateful to the NHANES team for their rigorous data collection and management efforts, which provided the empirical foundation for this study. Our heartfelt appreciation extends to the NHANES participants whose time and contributions made this research possible. We further acknowledge the valuable insights and critical feedback offered by our colleagues during the development of this manuscript. Finally, we thank our home institution for its administrative and technical support throughout the course of this work.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eYi-Chang Zhao, Shi-Qi Wu, Jia-Kai Li, Zhi-Hua Sun, Bi-Kui Zhang, Rao Fu, and Miao Yan took responsibility for the integrity and accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003eThe concept and design of the study were developed by Yi-Chang Zhao, Bi-Kui Zhang, Rao Fu, and Miao Yan. Data acquisition, analysis, or interpretation were performed by Yi-Chang Zhao, Shi-Qi Wu, Bi-Kui Zhang, Rao Fu, and Miao Yan. Yi-Chang Zhao and Miao Yan drafted the manuscript, while Rao Fu, and Miao Yan provided critical revisions for intellectual content. Statistical analysis was conducted by Yi-Chang Zhao and Shi-Qi Wu, and administrative, technical, or material support was provided by Shi-Qi Wu, and Yi-Chang Zhao. Supervision of the study was overseen by Bi-Kui Zhang and Miao Yan. Sensitivity and risk-of-bias assessments were conducted by Yi-Chang Zhao, and study selection and data collection were carried out by Yi-Chang Zhao, Rao Fu, and Miao Yan. All authors reviewed and approved the final manuscript, ensuring its scientific rigor and accuracy.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the Teaching Reform Program for Graduate Education at Central South University [Grant No. 2023JGB039], the Hunan Provincial Degree and Graduate Education Reform Project [Grant No. 2023JGYB041], and the Hunan Provincial Health High-Level Talent Scientific Research Project [Grant No. R2023061]. Additional funding was provided by the Research Project established by the Chinese Pharmaceutical Association Hospital Pharmacy Department [Grant No. CPA-Z05-ZC-2024002], and the Postgraduate Innovative Project of Central South University [Grant No. 2024XQLH030].\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eData supporting the findings of this study are publicly available from the official websites of the CHARLS (http://charls.pku.edu.cn) and the NHANES (https://www.cdc.gov/nchs/nhanes/index.htm). Access to both datasets is granted for research purposes upon request or registration, in accordance with their respective data use policies.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe China Health and Retirement Longitudinal Study was approved by the Ethics Review Committee of Peking University. The National Health and Nutrition Examination Survey were approved by the National Center for Health Statistics ethics review board. Written informed consent was obtained from all participants. Informed consent was obtained from each subject in these two cohorts.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis manuscript is not currently under consideration for publication elsewhere, and the work reported will not be submitted for publication elsewhere until a final decision has been made as to its acceptability by the journal.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAll authors declare no conflict of interest.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eDepartment of Pharmacy, the Second Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China 410011.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eInternational Research Center for Precision Medicine, Transformative Technology and Software Services, Hunan, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eNational Clinical Research Center for Metabolic Diseases, Changsha, Hunan, P.R. China 410011.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003eChina Pharmaceutical University, Nanjing, Jiangsu, P.R. China 210009.\u003c/p\u003e\n\u003cp\u003eAddress: Department of Clinical Pharmacy, the Second Xiangya Hospital of Central South University, Changsha 410010, Hunan Province, China.\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u003c/p\u003e\n\u003cp\u003eTel: 086-0731-85292098\u003c/p\u003e\n\u003cp\u003e# The two authors contributed equally to this work and share first authorship\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVogel B, Acevedo M, Appelman Y, et al. 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Nutrients. 2022;14(14). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu14142936\u003c/span\u003e\u003cspan address=\"10.3390/nu14142936\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Triglyceride-glucose index, Frailty index, Cardiovascular disease, Stroke, Population-based cohort","lastPublishedDoi":"10.21203/rs.3.rs-6835376/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6835376/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe triglyceride-glucose (TyG) index is a validated surrogate for insulin resistance, while frailty reflects cumulative physiological decline. The combined impact of TyG-Frailty Index (TyGFI) has not been adequately explored. This study aimed to investigate the association between TyGFI and the risk of cardiovascular disease (CVD) and stroke.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e A total of 5,448 participants from the China Health and Retirement Longitudinal Study (CHARLS) and 1,139 participants from the U.S. National Health and Nutrition Examination Survey (NHANES) were included. Multivariable logistic regression models were used to estimate associations with CVD and stroke, adjusting for demographic, clinical, and lifestyle covariates. Restricted cubic spline (RCS) and subgroup analyses were employed to examine dose-response relationships and interaction effects.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHigher TyGFI levels were associated with older age, adverse metabolic parameters, and increased prevalence of hypertension, diabetes, and dyslipidemia. In fully adjusted models, the highest TyGFI quartile was significantly associated with increased risks of CVD (CHARLS: OR 15.09, 95% CI: 9.65\u0026ndash;23.60; NHANES: OR 4.98, 95% CI: 2.04\u0026ndash;12.19) and stroke (CHARLS: OR 21.12, 95% CI: 6.44\u0026ndash;69.23; NHANES: OR 12.98, 95% CI: 2.58\u0026ndash;65.17), with consistent dose-response trends confirmed by RCS analyses. Subgroup analyses further demonstrated the robustness of these associations across diverse demographic and clinical strata.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTyGFI is a strong and independent predictor of CVD and stroke in two nationally representative cohorts. By integrating metabolic and functional risk dimensions, TyGFI provides a more comprehensive risk stratification tool, with significant implications for early identification and prevention of cardiovascular events in aging populations.\u003c/p\u003e","manuscriptTitle":"Predictive Value of the Combined TyG and FI Index for Cardiovascular Disease and Stroke in two prospective cohorts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 06:29:13","doi":"10.21203/rs.3.rs-6835376/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-03T04:56:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-03T01:37:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-01T10:37:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-14T08:44:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51536711341341604338957692130581490032","date":"2025-06-14T07:21:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287296979046283570378105813515529208533","date":"2025-06-13T03:47:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91766225023209156119111101562248254720","date":"2025-06-12T08:29:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172535522094922621865599245559652448113","date":"2025-06-10T21:38:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206142316728736486812583212400081579251","date":"2025-06-10T13:33:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-10T12:49:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-10T12:38:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2025-06-10T08:50:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e475cd46-0923-4a17-ba00-8110724560ae","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T15:58:34+00:00","versionOfRecord":{"articleIdentity":"rs-6835376","link":"https://doi.org/10.1186/s12933-025-02880-9","journal":{"identity":"cardiovascular-diabetology","isVorOnly":false,"title":"Cardiovascular Diabetology"},"publishedOn":"2025-08-04 15:56:55","publishedOnDateReadable":"August 4th, 2025"},"versionCreatedAt":"2025-06-16 06:29:13","video":"","vorDoi":"10.1186/s12933-025-02880-9","vorDoiUrl":"https://doi.org/10.1186/s12933-025-02880-9","workflowStages":[]},"version":"v1","identity":"rs-6835376","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6835376","identity":"rs-6835376","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}