Combined Associations of Triglyceride-Glucose Index (TyG) and Cystatin C with Stroke Incidence: A National Cohort Study

Combined Associations of Triglyceride-Glucose Index (TyG) and Cystatin C with Stroke Incidence: A National Cohort Study | 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 Combined Associations of Triglyceride-Glucose Index (TyG) and Cystatin C with Stroke Incidence: A National Cohort Study ChunTing Zhou, Tian She, Hua Li, Jinghao Rong, Min Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9229681/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background The triglyceride-glucose (TyG) index and cystatin C are recognized as valuable predictors of stroke risk. However, their combined predictive value for stroke remains unclear.This study aims to assess the association between these two biomarkers and investigate their potential interaction. Methods This cohort study included 4,483 adults aged ≥ 45 years from the China Health and Retirement Longitudinal Study, with up to 9 years of follow-up for incident stroke. Participants were grouped by TyG index (cutoff: 8.59) and cystatin C level (cutoff: 1.06 mg/L), with Cox models evaluating stroke risk differences across groups. Survival probabilities were estimated using the Kaplan–Meier method. Mediation analysis explored the interplay between the two biomarkers. Results Among 4,483 participants, 347 (7.7%) developed stroke over a 9-year follow-up period. Compared with controls, stroke risk was elevated in the high TyG index (HR = 1.29, 1.03–1.61), high cystatin C (HR = 1.26, 1.00-1.60), and especially the combined high-risk group (HR = 1.66, 1.18–2.33). Furthermore, mediation analysis suggested a significant mediating relationship between the TyG index and cystatin C. Conclusion Both the TyG index and cystatin C are strongly associated with the incidence of stroke. Their combined application enhances the predictive power for stroke events, with evidence of a significant mediating effect between them. Triglyceride-Glucose (TyG) Index Cystatin C Stroke CHARLS Figures Figure 1 Figure 2 Figure 3 Contributions to the Literature This study is the first to investigate the predictive value of the triglyceride-glucose (TyG) index combined with cystatin C for stroke. The findings specifically inform early clinical diagnosis of stroke and may influence clinical decision-making. This demonstrates the important potential value of combining examination indicators for improving public health. It reflects the current health status of the community population, aiming to enhance the practicality and convenience of fundamental health management. 1. Introduction Stroke, a disease resulting from impaired blood flow that causes neuronal damage, has become the leading cause of disability and mortality globally [ 1 ]. According to the Global Burden of Disease tudy, the global incidence of new stroke cases amounted to 11.9 million in 2021, marking a 70% increase from the 1990 level, while stroke-related mortality surpassed 7.3 million, representing a 44% rise relative to the 1990 baseline [ 2 ]. The global economic burden of stroke exceeds $ 891 billion a year, with projections indicating a continued upward trend [ 3 ]. As the country with the highest risk and burden of stroke, China accounts for approximately one-third of global stroke-related mortality [ 4 , 5 ]. In addition, challenges including accelerated population aging and the increasing incidence of stroke among younger individuals indicate that China's absolute stroke burden will continue to rise over the next three decades [ 6 ]. Recently, several novel therapies have emerged in the treatment of stroke, but these advancements still fail to significantly improve the prognostic outcome of stroke patients [ 7 ]. Therefore, it is crucial to identify modifiable risk factors and develop effective prevention strategies to reduce the incidence of stroke, improve patient outcomes, and alleviate the escalating disease burden. Insulin resistance(IR), defined as impaired tissue responsiveness to insulin levels, is associated with the development of multiple metabolic diseases and has become an important risk factor for stroke [ 8 , 9 ]. The hyperinsulinemic-euglycemic clamp test, regarded as the gold standard for assessing IR, has not been widely adopted in clinical practice owing to its procedural complexity and high cost [ 10 ]. Similarly, although the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) demonstrates strong predictive efficacy for cardiovascular and cerebrovascular events, its clinical utility is limited by the requirement for fasting insulin measurement [ 11 ]. The triglyceride-glucose (TyG) index, a robust and easily measurable predictor of IR with exceptional disease-predictive capability, has emerged as a promising biomarker in cardiovascular disease (CVD) research [ 12 ]. Studies have shown that the TyG index exhibits significant predictive value for the incidence of various diseases such as stroke [ 13 ] and acute kidney injury [ 14 ]. Notably, it demonstrates superior predictive performance for stroke compared to HOMA-IR [ 15 ]. Cystatin C, as a serum biomarker for assessing renal function, is recognized as a predictor of mortality and cardiovascular events risks in the elderly [ 16 ]. Studies have found that elevated cystatin C levels are not only independently associated with both ischemic and hemorrhagic stroke, but also can indicate the risk of cardiovascular events and death in patients with or without stroke [ 17 ]. Given that the TyG index exhibits enhanced predictive capability for cardiovascular risk when integrated with additional biomarkers [ 18 ], and the potential role of IR in the relationship between cystatin C and CVD remains unclear [ 19 ], further investigation is needed to examine the joint effect of the TyG index and cystatin C on stroke incidence. Currently, evidence remains insufficient regarding the joint impact of the TyG index and cystatin C on stroke outcomes. Therefore, we employed data from the China Health and Retirement Longitudinal Study (CHARLS) to examine the association between the TyG index in combination with cystatin C and the risk of stroke. 2. Methods 2.1 Study design and populations Our study utilized the CHARLS database, a nationally representative longitudinal cohort study of Chinese residents aged 45 years and above, which aims to assess multidimensional information on individuals' social, economic, and health status. The CHARLS study covers 150 regions in China, encompassing 10,257 households and 17,708 individual participants. The national baseline survey was conducted in 2011, with subsequent biennial follow-up surveys conducted at regular intervals. To date, four waves of follow-up surveys have been successfully conducted in 2013, 2015, 2018, and 2020, respectively. The initial cohort encompassed 17,705 participants at baseline. Subsequently, 13222 participants were excluded based on the following predefined criteria: (1) missing data on TyG and Cystatin C (n = 4,801); (2) participants with stroke (n = 371); (3) participants with kidney disease (n = 314); (4) age < 45 (n = 223); (5) lost to follow up (n = 6,785); (6) lack of BMI (n = 728).Ultimately, a total of 4,483 participants were included in the final analytical cohort. The screening process is shown in Fig. 1 . The study revealed that among participants with kidney disease, neither elevated TyG index nor increased cystatin C levels showed a significant association with a higher risk of stroke (Supplementary Table S1 and Supplementary Figure S1 ). This finding may be attributed to the fact that cystatin C, as a biomarker of renal function, is strongly influenced by the presence of kidney disease, potentially obscuring its independent relationship with stroke. To more accurately assess the true effect of cystatin C on stroke risk, these individuals were excluded from the analysis. The CHARLS study was conducted in strict accordance with the principles outlined in the Declaration of Helsinki and received ethical approval from the Peking University Institutional Review Board (IRB00001052-11015). All participants provided written informed consent prior to their involvement in the study. The research methodology complies with the reporting standards established by the Strengthening the Reporting of Observational Studies in Epidemiology statement. 2.2 Assessment of stroke The study outcome was defined as the first occurrence of stroke occurring between the baseline assessment and the fifth wave (2020) of the survey. Stroke outcomes were ascertained based on determined by the self-reported questionnaire item: " Have you been diagnosed with stroke by a doctor?" The onset timing of stroke was further obtained through the follow-up question: " When was stroke first diagnosed or known by yourself?" All questionnaire responses were administered by trained interviewers using standardized instruments and underwent rigorous validation procedures to ensure data accuracy. 2.3 Assessment of TyG and Cystatin C Fasting venous blood samples were collected by staff from the Chinese Center for Disease Control and Prevention in accordance with standardized protocols and subsequently analyzed in the central laboratory. The coefficients of variation were less than 5% for cystatin C and less than 2% for glucose and triglycerides, ensuring data reliability. The TyG index was calculated using the formula: TyG = ln[TG (mg/dL) × FBG (mg/dL)] [ 20 ]. 2.4 Covariates The covariates include: age, sex, marital status (married or unmarried), education level (college or others), living place (village or city), body mass index (BMI), drinking status, smoking status, and health conditions. Health conditions are determined based on respondents' self-reported medical histories. 2.5 Statistical analysis We examined the baseline characteristics of the participants using descriptive statistical methods (Supplementary Table S2). Continuous variables are presented as mean with standard deviation (SD), and categorical variables are described in terms of frequency and percentage. Baseline characteristics were summarized based on the joint assessment of the TyG index (8.59) and cystatin C (1.06 mg/L), with each category dichotomized into “low” and “high” groups. Accordingly, participants were classified into four categories for the analysis of baseline characteristics. Baseline characteristics were compared across groups using one-way analysis of variance for continuous variables and the chi-square test for categorical variables. We employed Kaplan-Meier survival analysis to estimate the cumulative incidence of stroke across four groups. Cumulative risk curves were generated using the Kaplan-Meier method, and between-group differences were assessed via the log-rank test. To explore the association between the TyG index, Cystatin C, and the risk of stroke development, we used a multivariable-adjusted Cox proportional hazards model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), with careful consideration of the time-to-event structure. Model 1 was adjusted for age and sex only; Model 2 was further adjusted for diabetes (yes or no), heart disease (yes or no), smoking status (yes or no), drinking status (yes or no), living place (village or city), education level (college or others), and marital status (married or unmarried), in addition to the variables included in Model 1. Subsequently, we evaluated the impact of the TyG index on stroke events stratified by cystatin C levels, as well as the effect of cystatin C levels on stroke events stratified by TyG index. We performed subgroup analyses stratified by age (< 59 or ≥ 60 years), sex (female or male), presence of heart disease (yes or no), presence of diabetes (yes or no), and smoking status (yes or no). Furthermore, we conducted a mediation analysis to investigate the direct and indirect associations between the TyG index and stroke events mediated by high cystatin C levels. Specifically, the high TyG index group (≥ 8.59) was defined as the exposure variable, the high cystatin C group (≥ 1.06 mg/L) as the mediator, and stroke events as the outcome. Similarly, we also investigated the mediating role of TYG in the association between cystatin C and stroke. All statistical analyses were conducted using R software (version 4.4.2). Mediation analysis was performed using the mediation package, and Cox regression analyses were carried out using the survival package. Statistical significance was defined as a two-tailed P value < 0.05. 3. Results 3.1 Descriptive statistics The study included 4,483 participants from the CHARLS dataset (2011–2020), with a average age of 59.2 ± 9.1 years. Among them, 1,941 (43.3%) were men and 2,542 (56.7%) were women. Based on the median TyG index (8.59) and the established cutoff for cystatin C (1.06), participants were categorized into four groups: low TyG index and low Cystatin C (Group 1): 1,522 (34.0%); low TyG index and high Cystatin C (Group 2): 727 (16.2%); high TyG index and low Cystatin C (Group 3): 1,626 (36.3%); and high TyG index and high Cystatin C (Group 4): 608 (13.5%). Among the groups with higher TyG index and/or cystatin C levels, older age, diabetes, or heart disease were more common. During the 9-year follow-up period, a total of 347 participants (7.7%) experienced a stroke. The incidence rate of stroke (per 1,000 person-years) varied significantly according to different levels of the TyG index and cystatin C: 5.80 per 1,000 person-years in group 1; 10.04 per 1,000 person-years in group 2; 9.52 per 1,000 person-years in group 3; 13.25 per 1,000 person-years in group 4. Figure 2 presents the Kaplan–Meier curves for cumulative stroke risk. The results indicate that the difference in cumulative risk between the high TyG or high cystatin C group and other groups becomes progressively more significant over time. In addition, participants with concurrent high TyG index and cystatin C levels exhibited the greatest risk of stroke. 3.2 Regression results Table 1 illustrates the associations between the TyG index, serum Cystatin C levels, and the risk of incident stroke after adjustment for multiple covariates. In Cox proportional hazards regression models, both the TyG index and Cystatin C were independently associated with an increased risk of stroke. A higher TyG index was associated with increased stroke risk (HR = 1.29, 95% CI: 1.03–1.61). Similarly, elevated cystatin C levels also raised the risk (HR = 1.26, 95% CI: 1.00–1.60). Notably, individuals with high levels of both biomarkers faced a substantially greater risk (HR = 1.66, 95% CI: 1.18–2.33) compared to the reference group. Table 1 Stroke risk following combined exposure stratified by TyG index and Cystatin C Variables Model 1 Model 2 HR(95% CI) P value HR(95% CI) P value Stroke(count/total) Low TyG(2249/4483) Ref Ref High TyG(2234/4483) 1.50(1.21–1.85) < 0.001 1.29(1.03–1.61) 0.025 Low Cystatin C(3148/4483) Ref Ref High Cystatin C(1335/4483) 1.31(1.03–1.66) 0.028 1.26(1.00-1.60) 0.054 Group 1(1522/4483) Ref Ref Group 2(727/4483) 1.50(1.06–2.12) 0.023 1.45(1.03–2.06) 0.035 Group 3(1626/4483) 1.64(1.24–2.16) < 0.001 1.42(1.07–1.89) 0.015 Group 4(608/4483) 2.00(1.43–2.80) < 0.001 1.66(1.18–2.33) 0.004 HR, hazard ratio; CI, confidence interval; TyG, triglyceride-glucose index. Model 1: age and gender were adjusted; model 2: age, gender, BMI, diabetes, heart disease, smoking, drinking, living place, education and marital statue were adjusted. Using restricted cubic splines, we examined the trends in the relationships between the TyG index, Cystatin C, and stroke risk across all participants (Supplementary Figure S2). In the fully adjusted model controlling for covariates, both the TyG index (P-nonlinear = 0.066) and Cystatin C (P-nonlinear = 0.790) demonstrated nonlinear associations with stroke incidence (P-Nonlinear > 0.05). 3.3 Mediation analysis The mutual mediating effects between the TyG index, cystatin C, and stroke events are illustrated in Fig. 3 . In this model, the direct effects of both high Cystatin C and high TyG index suggest that they are independently associated with the risk of stroke. However, high Cystatin C significantly mediated the association between high TyG index and stroke events by -9.7% (P < 0.001). Meanwhile, high TyG index also mediated the association between high Cystatin C and stroke risk by -18.6% (P < 0.001). Therefore, we found that the combination of TyG index and Cystatin C exerts a certain inhibitory effect on the increased stroke risk posed by each factor individually, indicating a masking effect. This suggests a more complex interrelationship between the TyG index and Cystatin C. 3.4 Subgroup analysis The subgroup analysis revealed significant interactions between the TyG index, Cystatin C, and factors including age and the presence of heart disease (P < 0.05; Supplementary Table S3). Furthermore, elevated levels of both the TyG index and Cystatin C were more strongly associated with an increased risk of stroke events among older individuals, patients with heart disease, group without diabetes, or non-smokers. 3.5 Sensitivity analysis To assess the robustness of our research findings, we performed a sensitivity analysis. First, regarding the handling of missing data, we substituted multiple imputation with the direct exclusion of missing values. The results indicated that participants with both a high TyG index and high Cystatin C had a significantly 65% higher risk of stroke compared with the control group (HR = 1.65, 95% CI: 1.17–2.33, supplementary Table S4). In addition, we stratified participants into low, medium, and high TyG index groups based on tertiles of the TyG index. The results revealed that, compared with the low TyG index group, the risk of stroke was significantly 38% and 42% higher in the medium and high TyG index groups, respectively (Supplementary Table S5). These findings further validate the accuracy of the results presented in this study. 4. Discussion This study is the first to explore the association between the TyG index combined with cystatin C and stroke in Chinese population. In a 9-year follow-up of 4,483 Chinese adults aged 45 years and above, the study found that participants exposed to elevated TyG indices and/or cystatin C exhibited a significantly higher risk of stroke incidence. The effect persisted significantly even after adjusting for relevant covariates. The analysis revealed that individuals with concurrent high levels of both the TyG index and cystatin C had a 66% higher risk of stroke compared to those with low levels of both biomarkers. Stratified analysis by subgroup showed that an increase in the TyG index and/or cystatin C index was significantly associated with a higher probability of stroke events, particularly among patients with heart disease, no diabetes, no smoking history, or aged 60 years and above. Sensitivity analysis supported the consistency of the key results described above. Accordingly, the integration of the TyG index and cystatin C emerges as a promising predictive biomarker, holding significant potential for the early identification of stroke in clinical practice. The TyG index, characterized by its simplicity, accessibility, and robust predictive capacity for disease risk, has emerged as a valuable surrogate biomarker for assessing IR [ 12 ]. Our research findings indicate that an elevated TyG index is significantly correlated with an increased risk of stroke. Specifically, individuals with a high TyG index exhibit a 29% higher risk of stroke compared to those in the control group. A 11-year follow-up study similarly demonstrated this association, showing that participants in the highest TyG index quartile had a 1.45-fold risk of stroke compared with those in the lowest quartile, which is comparable to the 29% higher risk observed in our study [ 13 ]. A growing body of evidence suggests that the TyG index is closely associated with both the development and progression of cardiovascular disease, and it has also been significantly correlated with the risk of all-cause mortality [ 21 , 22 ]. A meta-analysis comprising 28 studies demonstrated that a higher TyG index is significantly associated with an elevated risk of cerebrovascular disease [ 23 ]. The study conducted by Liu et al. further confirmed that the TyG index is significantly associated with stroke recurrence and unfavorable prognosis in patients [ 24 ]. Mechanistically, the potential relationship between the TyG index and stroke may be related to the progression of atherosclerosis and the synthesis of inflammatory markers [ 25 ]. Specifically, IR is associated with reduced nitric oxide (NO) production by endothelial cells and increased release of procoagulant factors. These alterations contribute to vasoconstriction, platelet activation and aggregation, as well as endothelial dysfunction, all of which play a pivotal role in the pathogenesis of atherosclerosis and thrombosis [ 26 ]. Additionally, IR can further aggravate vascular damage by inducing smooth muscle cell proliferation and the release of pro-inflammatory factors [ 27 ]. Furthermore, greater stroke severity is associated with higher levels of IR and more pronounced inflammatory response [ 25 ]. Given that IR plays a pivotal role in the development of diabetes, we further conducted a subgroup analysis according to diabetes status to assess whether blood glucose levels affect the aforementioned effects and mechanisms. The results indicate that a higher TyG index is more strongly associated with an increased risk of strokein the group without diabetes than in person with diabetes. Specifically, among the group without diabetes, those with a high TyG index had a significantly increased risk of stroke, with the risk being elevated by approximately 60% to 93%. A prospective study of 17,708 persons without diabetes individuals revealed that, compared with those in the lowest quartile of the TyG index, individuals in the other quartiles had a 64%–74% higher risk of stroke—an increase that closely aligns with the 60%–93% elevation observed in our study [ 28 ]. However, yang et al. 's research found that there was no significant association between the TyG index and the recurrence and prognosis of stroke. The area under the ROC curve of the TyG index was only 0.56, indicating its limited value in predicting stroke recurrence and mortality among the group without diabetes [ 29 ]. A study based on the association between different TyG-related indices under different glucose statuses and the risk of stroke found that the triglyceride-glucose body mass index (TYG-BMI) and triglyceride-glucose waist circumference (TYG-WC) indices had significant predictive capabilities in the group without diabetes, while the TyG index had better predictability in person with diabetes [ 30 ]. This emphasizes the importance of identifying appropriate predictive biomarkers that are tailored to individuals with distinct glucose statuses, thereby facilitating more accurate and individualized clinical decision-making by clinicians for patients at different stages of diabetes. Cystatin C is a non-glycosylated, basic protein secreted by all nucleated cells and metabolized primarily in the proximal renal tubules. It serves as a reliable biomarker for evaluating renal function [ 31 ]. More and more research evidence has revealed that cystatin C also shows great potential in assessing cardiovascular events [ 16 , 32 ]. A meta-analysis of 14 studies from 8 countries, including China, demonstrated that individuals with the highest cystatin C levels had a 162% higher risk of CVD and an 83% higher risk of stroke compared with those in the lowest tertile. Notably, the association with fatal CVD was even more pronounced [ 33 ]. Similarly, our study found that the risk of stroke was significantly 26% higher in the high cystatin C group compared with the control group, lower than 83% in the meta-study, which might be related to the difference in population distribution. A Mendelian randomization analysis of 16 prospective cohorts revealed no evidence of a causal association between cystatin C levels and the risk of stroke, implying that cystatin C is unlikely to be a causative factor in stroke pathogenesis [ 34 ]. Another genetic study, utilizing data from the UK Biobank, identified loci associated with cystatin C. Through Mendelian randomization analysis, the study confirmed that cystatin C exerts a promoting effect on stroke, thereby providing genetic-level evidence for an association between cystatin C and stroke risk [ 35 ]. The aforementioned research is controversial regarding the existence of a causal relationship between cystatin C and stroke. However, it is evident that cystatin C, as a biomarker, exhibits predictive value for stroke risk. Nevertheless, further research is required to elucidate the underlying mechanisms linking them. The influence of high cystatin C on the occurrence of stroke may be related to its regulation of inflammatory processes, involvement in atherosclerosis development, effects on vascular wall remodeling, and potential direct cytotoxic actions, etc. [ 17 , 36 ]. On the other hand, some studies propose that cystatin C plays a pivotal role in neuroprotection induced by hyperbaric oxygen (HBO) preconditioning, suggesting that there may be a bidirectional effect between cystatin C and stroke [ 37 ]. The results of the restricted cubic spline analysis indicated nonlinear associations between both the TyG index and Cystatin C and the risk of stroke, providing further support for this hypothesis. Specifically, the CATIS study found a negative correlation between cystatin C levels and post-stroke cognitive impairment [ 38 ], whereas the Health ABC study showed a positive association between high cystatin C levels and cognitive dysfunction in older adults [ 39 ]. Currently, there is some controversy over the neuroprotective effect of cystatin C in clinical evidence. The results of the aforementioned studies suggest that cystatin C may serve as a reliable biomarker for stroke prediction. However, its utility as an interventional target for reducing the risk of stroke and improving clinical outcomes remains limited, and further evidence is required to establish its therapeutic potential [ 34 ]. Subgroup analysis stratified by the presence or absence of heart disease revealed that, compared with participants without heart disease, those with heart disease exhibited a significantly greater increase in stroke risk with elevated cystatin C levels. Collectively, the existing evidence underscores that cystatin C serves not only as a marker of renal function but also exhibits a robust correlation with cardiovascular health [ 40 , 41 ]. To the best of our knowledge, no previous studies have investigated the predictive value of the TyG index combined with cystatin C for stroke in the Chinese population. Therefore, this study aims to evaluate the long-term predictive value of the TyG index combined with cystatin C for stroke risk in the Chinese population aged 45 years and older without kidney disease. Our findings indicate that the combination of the TyG index and cystatin C demonstrates superior predictive value for stroke occurrence compared to either marker alone. Lee et al. demonstrated that IR and cystatin C synergistically contribute to the development and progression of CVD in patients with type 2 diabetes by jointly mediating inflammatory pathways [ 19 ]. In the mediation analysis, a significant interaction was observed between the TyG index and cystatin C. Notably, the mediating effect between the two was non-positive, indicating a potentially complex regulatory mechanism, which may involve metabolic compensation. Although the results indicate a potential inhibitory interaction between the two factors, the direct effect remains dominant, thereby obscuring the mediating effect’s negative contribution. Nonetheless, stroke risk exhibits the most pronounced increase when both factors are simultaneously elevated. In summary, the combined use of the TyG index and cystatin C markedly improves the predictive ability for stroke. Both the TyG index and cystatin C are non-invasive biomarkers that are readily accessible and widely utilized in clinical settings. Their integration into routine clinical practice holds promise as a novel strategy for the early identification and risk stratification of individuals at high risk of stroke, thereby facilitating timely preventive interventions. Additional research is warranted to clarify the potential roles and mechanistic pathways of the TyG index and cystatin C in stroke development. While this study yields meaningful findings, some limitations merit further consideration. First, as an observational study, the findings may be influenced by residual confounding factors, such as psychological characteristics and household income. Consequently, it is not possible to establish a causal relationship between the TyG index, cystatin C, and stroke. Second, the standard cutoff values for the TyG index have not yet been universally established. In the current analysis, median stratification was employed, which may not accurately reflect clinically relevant threshold values. Third, stroke outcomes were determined solely based on self-reported physician diagnoses, which may be subject to recall bias. Fourth, patients with kidney disease were excluded from the study population, limiting the generalizability of the findings to the general population, which warrants further investigation. Conclusion Both elevated TyG index and elevated serum cystatin C levels were independently associated with a significantly higher risk of stroke in individuals without renal disease. The concurrent use of these biomarkers conferred improved predictive accuracy for stroke events. Interestingly, a significant negative interaction was observed between the TyG index and cystatin C; however, the pathophysiological basis of this interaction remains uncertain and merits further mechanistic inquiry. Abbreviations BMI Body mass index CHARLS China Health and Retirement Longitudinal Study CI Confidence interval CVD Cardiovascular disease HBO Hyperbaric oxygen HOMA-IR Homeostasis model assessment for insulin resistance HR Hazard ratio IR Insulin resistance NO Nitric oxide SD Standard deviation TyG Triglyceride-glucose TyG-BMI Triglyceride-glucose body mass index TyG-WC triglyceride-glucose waist circumference Declarations Ethics approval and consent to participate The CHARLS study received ethical approval from the Peking University Institutional Review Board (IRB00001052-11015). Consent for publication All participants provided written informed consent prior to their involvement in the study. Availability of data and materials The datasets generated and analysed during the current study are available in the CHARLS repository, https://charls.pku.edu.cn Competing interests The authors declare that they have no competing interests Funding None Authors' contributions ChunTing Zhou : Conceptualization, Writing - Review & Editing, Supervision，Project administration, Tian She ： Formal analysis, Resources, Data Curation, Writing - Original Draft, Visualization, Hua Li ： Conceptualization, Writing - Review & Editing, Supervision, Project administration , Jinghao Rong ： Methodology, Software, Validation Writing - Original Draft, Visualization, Min Yu: Supervision, Project administration. ChunTing Zhou and Tian She contributed equally. Clinical trial number: not applicable References Hilkens NA, Casolla B, Leung TW, de Leeuw F-E, Stroke. Lancet. 2024;403:2820–36. GBD 2021 Stroke Risk Factor Collaborators. 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Insulin resistance and inflammation may have an additional role in the link between cystatin C and cardiovascular disease in type 2 diabetes mellitus patients. Metabolism. 2010;59:241–6. Tahapary DL, Pratisthita LB, Fitri NA, Marcella C, Wafa S, Kurniawan F, et al. Challenges in the diagnosis of insulin resistance: Focusing on the role of HOMA-IR and tryglyceride/glucose index. Diabetes Metab Syndr. 2022;16:102581. Huang R, Wang Z, Chen J, Bao X, Xu N, Guo S, et al. Prognostic value of triglyceride glucose (TyG) index in patients with acute decompensated heart failure. Cardiovasc Diabetol. 2022;21:88. Zhang R, Shi S, Chen W, Wang Y, Lin X, Zhao Y, et al. Independent effects of the triglyceride-glucose index on all-cause mortality in critically ill patients with coronary heart disease: Analysis of the MIMIC-III database. Cardiovasc Diabetol. 2023;22:10. Yan F, Yan S, Wang J, Cui Y, Chen F, Fang F, et al. Association between triglyceride glucose index and risk of cerebrovascular disease: Systematic review and meta-analysis. Cardiovasc Diabetol. 2022;21:226. Liu D, Yang K, Gu H, Li Z, Wang Y, Wang Y. Predictive effect of triglyceride-glucose index on clinical events in patients with acute ischemic stroke and type 2 diabetes mellitus. Cardiovasc Diabetol. 2022;21:280. Jin A, Wang S, Li J, Wang M, Lin J, Li H, et al. Mediation of systemic inflammation on insulin resistance and prognosis of nondiabetic patients with ischemic stroke. Stroke. 2023;54:759–69. Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuñiga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17:122. Grandl G, Wolfrum C. Hemostasis, endothelial stress, inflammation, and the metabolic syndrome. Semin Immunopathol. 2018;40:215–24. Yu Y, Meng Y, Liu J. Association between the triglyceride-glucose index and stroke in middle-aged and older non-diabetic population: A prospective cohort study. Nutr Metab Cardiovasc Dis. 2023;33:1684–92. Yang X, Wang G, Jing J, Wang A, Zhang X, Jia Q, et al. Association of triglyceride-glucose index and stroke recurrence among nondiabetic patients with acute ischemic stroke. BMC Neurol. 2022;22:79. Bian K, Hou C, Jin H, Feng X, Peng M, Zhao X, et al. Association between triglyceride-glucose indices and ischemic stroke risk across different glucose metabolism statuses. Diabetes Res Clin Pract. 2025;222:112064. Coll E, Botey A, Alvarez L, Poch E, Quintó L, Saurina A, et al. Serum cystatin C as a new marker for noninvasive estimation of glomerular filtration rate and as a marker for early renal impairment. Am J Kidney Dis. 2000;36:29–34. West M, Kirby A, Stewart RA, Blankenberg S, Sullivan D, White HD, et al. Circulating cystatin C is an independent risk marker for cardiovascular outcomes, development of renal impairment, and long-term mortality in patients with stable coronary heart disease: The LIPID study. J Am Heart Assoc. 2022;11:e020745. Lee M, Saver JL, Huang W-H, Chow J, Chang K-H, Ovbiagele B. Impact of elevated cystatin C level on cardiovascular disease risk in predominantly high cardiovascular risk populations: A meta-analysis. Circ Cardiovasc Qual Outcomes. 2010;3:675–83. van der Laan SW, Fall T, Soumaré A, Teumer A, Sedaghat S, Baumert J, et al. Cystatin C and cardiovascular disease: A mendelian randomization study. J Am Coll Cardiol. 2016;68:934–45. Sinnott-Armstrong N, Tanigawa Y, Amar D, Mars N, Benner C, Aguirre M, et al. Genetics of 35 blood and urine biomarkers in the UK biobank. Nat Genet. 2021;53:185–94. Zhang J, Wu X, Gao P, Yan P. Correlations of serum cystatin C and glomerular filtration rate with vascular lesions and severity in acute coronary syndrome. BMC Cardiovasc Disord. 2017;17:47. Fang Z, Deng J, Wu Z, Dong B, Wang S, Chen X, et al. Cystatin C is a crucial endogenous protective determinant against stroke. Stroke. 2017;48:436–44. Guo D-X, Zhu Z-B, Zhong C-K, Bu X-Q, Chen L-H, Xu T, et al. Serum cystatin C levels are negatively correlated with post-stroke cognitive dysfunction. Neural Regen Res. 2020;15:922–8. Yaffe K, Lindquist K, Shlipak MG, Simonsick E, Fried L, Rosano C, et al. Cystatin C as a marker of cognitive function in elders: Findings from the health ABC study. Ann Neurol. 2008;63:798–802. Chang Z, Zou H, Xie Z, Deng B, Que R, Huang Z, et al. Cystatin C is a potential predictor of unfavorable outcomes for cerebral ischemia with intravenous tissue plasminogen activator treatment: A multicenter prospective nested case-control study. Eur J Neurol. 2021;28:1265–74. Correa S, Morrow DA, Braunwald E, Davies RY, Goodrich EL, Murphy SA, et al. Cystatin C for risk stratification in patients after an acute coronary syndrome. J Am Heart Assoc. 2018;7:e009077. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 29 Apr, 2026 Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor invited by journal 31 Mar, 2026 Editor assigned by journal 30 Mar, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 26 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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. 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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-9229681","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631970899,"identity":"9cdbb377-5c8d-4b60-b6e0-79478d4a04a2","order_by":0,"name":"ChunTing Zhou","email":"","orcid":"","institution":"Wuhan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"ChunTing","middleName":"","lastName":"Zhou","suffix":""},{"id":631970902,"identity":"d0d29697-de57-49fc-b893-b7e41c5b42f0","order_by":1,"name":"Tian She","email":"","orcid":"","institution":"Wuhan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Tian","middleName":"","lastName":"She","suffix":""},{"id":631970904,"identity":"9d6fffe4-ffa9-4d1e-9715-9e65bf15be19","order_by":2,"name":"Hua Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACPmYeIFkB5fEQo4UNrOUMiEW0FpAyxjaStLDzHpP4Oe9w4vz5DYwP3rYxyJsTdhhfmmTvtsOJG44xMBvObWMw3NlAUAuPmQTvttuJG9gY2KR52xgSDA4QoUXy75zbifPbGNh/E61FmrfhdmLDMSCbWC3G1jLH/htvOJbYLDnnnIThBkJa+PnPGN58U5MmO7/58MEPb8ps5AnaggQYG4CEBPHqR8EoGAWjYBTgBgBrqzY3gkhEMQAAAABJRU5ErkJggg==","orcid":"","institution":"Wuhan University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Hua","middleName":"","lastName":"Li","suffix":""},{"id":631970907,"identity":"5a2ababc-65c3-444d-8c35-c15f5a0482da","order_by":3,"name":"Jinghao Rong","email":"","orcid":"","institution":"Wuhan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jinghao","middleName":"","lastName":"Rong","suffix":""},{"id":631970913,"identity":"6d9891f7-cea8-4729-a4a5-b4ef5449ad80","order_by":4,"name":"Min Yu","email":"","orcid":"","institution":"Wuhan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2026-03-26 05:54:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9229681/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9229681/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108803550,"identity":"dd9412b6-3375-4da7-8523-531bfbb3a7d2","added_by":"auto","created_at":"2026-05-08 14:59:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90677,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of study population screening and classification\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9229681/v1/209695ceb0b5acd2f9858b99.png"},{"id":108399007,"identity":"8b0c93af-44a4-4b9c-a5aa-90c4a32aec0d","added_by":"auto","created_at":"2026-05-04 08:36:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76582,"visible":true,"origin":"","legend":"\u003cp\u003eK-M plot of Stroke risk by TyG index and Cystatin C. Median of TyG index:8.59, cutoff of Cystatin C:1.06.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9229681/v1/2536b59bfc6940467bed4256.png"},{"id":108493873,"identity":"52938414-d3eb-4138-970c-e146b0f34ed7","added_by":"auto","created_at":"2026-05-05 10:01:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67992,"visible":true,"origin":"","legend":"\u003cp\u003eMutual mediation effects of the TyG index and Cystatin C on stroke. The mediator is Cystatin C(\u003cstrong\u003eA\u003c/strong\u003e), TyG(\u003cstrong\u003eB\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9229681/v1/644ff6f3a690ea0732e04512.png"},{"id":109067555,"identity":"6a81006b-7f77-4b1b-ba45-03bb81b2acad","added_by":"auto","created_at":"2026-05-12 09:55:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":441601,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9229681/v1/785e3f42-edc5-4514-b6a2-86dc2b0acdd8.pdf"},{"id":108399005,"identity":"bcd7eca9-880d-4f70-a8aa-b258608094d5","added_by":"auto","created_at":"2026-05-04 08:36:29","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":108512,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9229681/v1/446e4af962b0ae58371c23e3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combined Associations of Triglyceride-Glucose Index (TyG) and Cystatin C with Stroke Incidence: A National Cohort Study","fulltext":[{"header":"Contributions to the Literature","content":"\u003col\u003e\n \u003cli\u003eThis study is the first to investigate the predictive value of the triglyceride-glucose (TyG) index combined with cystatin C for stroke.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe findings specifically inform early clinical diagnosis of stroke and may influence clinical decision-making.\u003c/li\u003e\n \u003cli\u003eThis demonstrates the important potential value of combining examination indicators for improving public health.\u003c/li\u003e\n \u003cli\u003eIt reflects the current health status of the community population, aiming to enhance the practicality and convenience of fundamental health management.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eStroke, a disease resulting from impaired blood flow that causes neuronal damage, has become the leading cause of disability and mortality globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the Global Burden of Disease tudy, the global incidence of new stroke cases amounted to 11.9\u0026nbsp;million in 2021, marking a 70% increase from the 1990 level, while stroke-related mortality surpassed 7.3\u0026nbsp;million, representing a 44% rise relative to the 1990 baseline [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The global economic burden of stroke exceeds \u003cspan\u003e$\u003c/span\u003e891\u0026nbsp;billion a year, with projections indicating a continued upward trend [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As the country with the highest risk and burden of stroke, China accounts for approximately one-third of global stroke-related mortality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition, challenges including accelerated population aging and the increasing incidence of stroke among younger individuals indicate that China's absolute stroke burden will continue to rise over the next three decades [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recently, several novel therapies have emerged in the treatment of stroke, but these advancements still fail to significantly improve the prognostic outcome of stroke patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, it is crucial to identify modifiable risk factors and develop effective prevention strategies to reduce the incidence of stroke, improve patient outcomes, and alleviate the escalating disease burden.\u003c/p\u003e \u003cp\u003eInsulin resistance(IR), defined as impaired tissue responsiveness to insulin levels, is associated with the development of multiple metabolic diseases and has become an important risk factor for stroke [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The hyperinsulinemic-euglycemic clamp test, regarded as the gold standard for assessing IR, has not been widely adopted in clinical practice owing to its procedural complexity and high cost [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Similarly, although the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) demonstrates strong predictive efficacy for cardiovascular and cerebrovascular events, its clinical utility is limited by the requirement for fasting insulin measurement [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The triglyceride-glucose (TyG) index, a robust and easily measurable predictor of IR with exceptional disease-predictive capability, has emerged as a promising biomarker in cardiovascular disease (CVD) research [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Studies have shown that the TyG index exhibits significant predictive value for the incidence of various diseases such as stroke [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and acute kidney injury [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Notably, it demonstrates superior predictive performance for stroke compared to HOMA-IR [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Cystatin C, as a serum biomarker for assessing renal function, is recognized as a predictor of mortality and cardiovascular events risks in the elderly [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Studies have found that elevated cystatin C levels are not only independently associated with both ischemic and hemorrhagic stroke, but also can indicate the risk of cardiovascular events and death in patients with or without stroke [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Given that the TyG index exhibits enhanced predictive capability for cardiovascular risk when integrated with additional biomarkers [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and the potential role of IR in the relationship between cystatin C and CVD remains unclear [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], further investigation is needed to examine the joint effect of the TyG index and cystatin C on stroke incidence.\u003c/p\u003e \u003cp\u003eCurrently, evidence remains insufficient regarding the joint impact of the TyG index and cystatin C on stroke outcomes. Therefore, we employed data from the China Health and Retirement Longitudinal Study (CHARLS) to examine the association between the TyG index in combination with cystatin C and the risk of stroke.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and populations\u003c/h2\u003e \u003cp\u003eOur study utilized the CHARLS database, a nationally representative longitudinal cohort study of Chinese residents aged 45 years and above, which aims to assess multidimensional information on individuals' social, economic, and health status. The CHARLS study covers 150 regions in China, encompassing 10,257 households and 17,708 individual participants. The national baseline survey was conducted in 2011, with subsequent biennial follow-up surveys conducted at regular intervals. To date, four waves of follow-up surveys have been successfully conducted in 2013, 2015, 2018, and 2020, respectively.\u003c/p\u003e \u003cp\u003eThe initial cohort encompassed 17,705 participants at baseline. Subsequently, 13222 participants were excluded based on the following predefined criteria: (1) missing data on TyG and Cystatin C (n\u0026thinsp;=\u0026thinsp;4,801); (2) participants with stroke (n\u0026thinsp;=\u0026thinsp;371); (3) participants with kidney disease (n\u0026thinsp;=\u0026thinsp;314); (4) age\u0026thinsp;\u0026lt;\u0026thinsp;45 (n\u0026thinsp;=\u0026thinsp;223); (5) lost to follow up (n\u0026thinsp;=\u0026thinsp;6,785); (6) lack of BMI (n\u0026thinsp;=\u0026thinsp;728).Ultimately, a total of 4,483 participants were included in the final analytical cohort. The screening process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The study revealed that among participants with kidney disease, neither elevated TyG index nor increased cystatin C levels showed a significant association with a higher risk of stroke (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This finding may be attributed to the fact that cystatin C, as a biomarker of renal function, is strongly influenced by the presence of kidney disease, potentially obscuring its independent relationship with stroke. To more accurately assess the true effect of cystatin C on stroke risk, these individuals were excluded from the analysis.\u003c/p\u003e \u003cp\u003e The CHARLS study was conducted in strict accordance with the principles outlined in the Declaration of Helsinki and received ethical approval from the Peking University Institutional Review Board (IRB00001052-11015). All participants provided written informed consent prior to their involvement in the study. The research methodology complies with the reporting standards established by the Strengthening the Reporting of Observational Studies in Epidemiology statement.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Assessment of stroke\u003c/h2\u003e \u003cp\u003eThe study outcome was defined as the first occurrence of stroke occurring between the baseline assessment and the fifth wave (2020) of the survey. Stroke outcomes were ascertained based on determined by the self-reported questionnaire item: \" Have you been diagnosed with stroke by a doctor?\" The onset timing of stroke was further obtained through the follow-up question: \" When was stroke first diagnosed or known by yourself?\" All questionnaire responses were administered by trained interviewers using standardized instruments and underwent rigorous validation procedures to ensure data accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Assessment of TyG and Cystatin C\u003c/h2\u003e \u003cp\u003eFasting venous blood samples were collected by staff from the Chinese Center for Disease Control and Prevention in accordance with standardized protocols and subsequently analyzed in the central laboratory. The coefficients of variation were less than 5% for cystatin C and less than 2% for glucose and triglycerides, ensuring data reliability. The TyG index was calculated using the formula: TyG\u0026thinsp;=\u0026thinsp;ln[TG (mg/dL) \u0026times; FBG (mg/dL)] [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariates\u003c/h2\u003e \u003cp\u003eThe covariates include: age, sex, marital status (married or unmarried), education level (college or others), living place (village or city), body mass index (BMI), drinking status, smoking status, and health conditions. Health conditions are determined based on respondents' self-reported medical histories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eWe examined the baseline characteristics of the participants using descriptive statistical methods (Supplementary Table S2). Continuous variables are presented as mean with standard deviation (SD), and categorical variables are described in terms of frequency and percentage. Baseline characteristics were summarized based on the joint assessment of the TyG index (8.59) and cystatin C (1.06 mg/L), with each category dichotomized into \u0026ldquo;low\u0026rdquo; and \u0026ldquo;high\u0026rdquo; groups. Accordingly, participants were classified into four categories for the analysis of baseline characteristics. Baseline characteristics were compared across groups using one-way analysis of variance for continuous variables and the chi-square test for categorical variables.\u003c/p\u003e \u003cp\u003eWe employed Kaplan-Meier survival analysis to estimate the cumulative incidence of stroke across four groups. Cumulative risk curves were generated using the Kaplan-Meier method, and between-group differences were assessed via the log-rank test. To explore the association between the TyG index, Cystatin C, and the risk of stroke development, we used a multivariable-adjusted Cox proportional hazards model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), with careful consideration of the time-to-event structure. Model 1 was adjusted for age and sex only; Model 2 was further adjusted for diabetes (yes or no), heart disease (yes or no), smoking status (yes or no), drinking status (yes or no), living place (village or city), education level (college or others), and marital status (married or unmarried), in addition to the variables included in Model 1. Subsequently, we evaluated the impact of the TyG index on stroke events stratified by cystatin C levels, as well as the effect of cystatin C levels on stroke events stratified by TyG index.\u003c/p\u003e \u003cp\u003eWe performed subgroup analyses stratified by age (\u0026lt;\u0026thinsp;59 or \u0026ge;\u0026thinsp;60 years), sex (female or male), presence of heart disease (yes or no), presence of diabetes (yes or no), and smoking status (yes or no). Furthermore, we conducted a mediation analysis to investigate the direct and indirect associations between the TyG index and stroke events mediated by high cystatin C levels. Specifically, the high TyG index group (\u0026ge;\u0026thinsp;8.59) was defined as the exposure variable, the high cystatin C group (\u0026ge;\u0026thinsp;1.06 mg/L) as the mediator, and stroke events as the outcome. Similarly, we also investigated the mediating role of TYG in the association between cystatin C and stroke.\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using R software (version 4.4.2). Mediation analysis was performed using the mediation package, and Cox regression analyses were carried out using the survival package. Statistical significance was defined as a two-tailed P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive statistics\u003c/h2\u003e \u003cp\u003eThe study included 4,483 participants from the CHARLS dataset (2011\u0026ndash;2020), with a average age of 59.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1 years. Among them, 1,941 (43.3%) were men and 2,542 (56.7%) were women. Based on the median TyG index (8.59) and the established cutoff for cystatin C (1.06), participants were categorized into four groups: low TyG index and low Cystatin C (Group 1): 1,522 (34.0%); low TyG index and high Cystatin C (Group 2): 727 (16.2%); high TyG index and low Cystatin C (Group 3): 1,626 (36.3%); and high TyG index and high Cystatin C (Group 4): 608 (13.5%). Among the groups with higher TyG index and/or cystatin C levels, older age, diabetes, or heart disease were more common.\u003c/p\u003e \u003cp\u003eDuring the 9-year follow-up period, a total of 347 participants (7.7%) experienced a stroke. The incidence rate of stroke (per 1,000 person-years) varied significantly according to different levels of the TyG index and cystatin C: 5.80 per 1,000 person-years in group 1; 10.04 per 1,000 person-years in group 2; 9.52 per 1,000 person-years in group 3; 13.25 per 1,000 person-years in group 4.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the Kaplan\u0026ndash;Meier curves for cumulative stroke risk. The results indicate that the difference in cumulative risk between the high TyG or high cystatin C group and other groups becomes progressively more significant over time. In addition, participants with concurrent high TyG index and cystatin C levels exhibited the greatest risk of stroke.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Regression results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the associations between the TyG index, serum Cystatin C levels, and the risk of incident stroke after adjustment for multiple covariates. In Cox proportional hazards regression models, both the TyG index and Cystatin C were independently associated with an increased risk of stroke. A higher TyG index was associated with increased stroke risk (HR\u0026thinsp;=\u0026thinsp;1.29, 95% CI: 1.03\u0026ndash;1.61). Similarly, elevated cystatin C levels also raised the risk (HR\u0026thinsp;=\u0026thinsp;1.26, 95% CI: 1.00\u0026ndash;1.60). Notably, individuals with high levels of both biomarkers faced a substantially greater risk (HR\u0026thinsp;=\u0026thinsp;1.66, 95% CI: 1.18\u0026ndash;2.33) compared to the reference group.\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\u003eStroke risk following combined exposure stratified by TyG index and Cystatin C\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eStroke(count/total)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow TyG(2249/4483)\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 \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh TyG(2234/4483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50(1.21\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29(1.03\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Cystatin C(3148/4483)\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 \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Cystatin C(1335/4483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31(1.03\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26(1.00-1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1(1522/4483)\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 \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 2(727/4483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50(1.06\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45(1.03\u0026ndash;2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 3(1626/4483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64(1.24\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42(1.07\u0026ndash;1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 4(608/4483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00(1.43\u0026ndash;2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.66(1.18\u0026ndash;2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHR, hazard ratio; CI, confidence interval; TyG, triglyceride-glucose index.\u003c/p\u003e \u003cp\u003eModel 1: age and gender were adjusted; model 2: age, gender, BMI, diabetes, heart disease, smoking, drinking, living place, education and marital statue were adjusted.\u003c/p\u003e \u003cp\u003eUsing restricted cubic splines, we examined the trends in the relationships between the TyG index, Cystatin C, and stroke risk across all participants (Supplementary Figure S2). In the fully adjusted model controlling for covariates, both the TyG index (P-nonlinear\u0026thinsp;=\u0026thinsp;0.066) and Cystatin C (P-nonlinear\u0026thinsp;=\u0026thinsp;0.790) demonstrated nonlinear associations with stroke incidence (P-Nonlinear\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Mediation analysis\u003c/h2\u003e \u003cp\u003eThe mutual mediating effects between the TyG index, cystatin C, and stroke events are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In this model, the direct effects of both high Cystatin C and high TyG index suggest that they are independently associated with the risk of stroke. However, high Cystatin C significantly mediated the association between high TyG index and stroke events by -9.7% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Meanwhile, high TyG index also mediated the association between high Cystatin C and stroke risk by -18.6% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Therefore, we found that the combination of TyG index and Cystatin C exerts a certain inhibitory effect on the increased stroke risk posed by each factor individually, indicating a masking effect. This suggests a more complex interrelationship between the TyG index and Cystatin C.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Subgroup analysis\u003c/h2\u003e \u003cp\u003eThe subgroup analysis revealed significant interactions between the TyG index, Cystatin C, and factors including age and the presence of heart disease (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Supplementary Table S3). Furthermore, elevated levels of both the TyG index and Cystatin C were more strongly associated with an increased risk of stroke events among older individuals, patients with heart disease, group without diabetes, or non-smokers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eTo assess the robustness of our research findings, we performed a sensitivity analysis. First, regarding the handling of missing data, we substituted multiple imputation with the direct exclusion of missing values. The results indicated that participants with both a high TyG index and high Cystatin C had a significantly 65% higher risk of stroke compared with the control group (HR\u0026thinsp;=\u0026thinsp;1.65, 95% CI: 1.17\u0026ndash;2.33, supplementary Table S4). In addition, we stratified participants into low, medium, and high TyG index groups based on tertiles of the TyG index. The results revealed that, compared with the low TyG index group, the risk of stroke was significantly 38% and 42% higher in the medium and high TyG index groups, respectively (Supplementary Table S5). These findings further validate the accuracy of the results presented in this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study is the first to explore the association between the TyG index combined with cystatin C and stroke in Chinese population. In a 9-year follow-up of 4,483 Chinese adults aged 45 years and above, the study found that participants exposed to elevated TyG indices and/or cystatin C exhibited a significantly higher risk of stroke incidence. The effect persisted significantly even after adjusting for relevant covariates. The analysis revealed that individuals with concurrent high levels of both the TyG index and cystatin C had a 66% higher risk of stroke compared to those with low levels of both biomarkers. Stratified analysis by subgroup showed that an increase in the TyG index and/or cystatin C index was significantly associated with a higher probability of stroke events, particularly among patients with heart disease, no diabetes, no smoking history, or aged 60 years and above. Sensitivity analysis supported the consistency of the key results described above. Accordingly, the integration of the TyG index and cystatin C emerges as a promising predictive biomarker, holding significant potential for the early identification of stroke in clinical practice.\u003c/p\u003e \u003cp\u003eThe TyG index, characterized by its simplicity, accessibility, and robust predictive capacity for disease risk, has emerged as a valuable surrogate biomarker for assessing IR [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Our research findings indicate that an elevated TyG index is significantly correlated with an increased risk of stroke. Specifically, individuals with a high TyG index exhibit a 29% higher risk of stroke compared to those in the control group. A 11-year follow-up study similarly demonstrated this association, showing that participants in the highest TyG index quartile had a 1.45-fold risk of stroke compared with those in the lowest quartile, which is comparable to the 29% higher risk observed in our study [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A growing body of evidence suggests that the TyG index is closely associated with both the development and progression of cardiovascular disease, and it has also been significantly correlated with the risk of all-cause mortality [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A meta-analysis comprising 28 studies demonstrated that a higher TyG index is significantly associated with an elevated risk of cerebrovascular disease [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The study conducted by Liu et al. further confirmed that the TyG index is significantly associated with stroke recurrence and unfavorable prognosis in patients [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Mechanistically, the potential relationship between the TyG index and stroke may be related to the progression of atherosclerosis and the synthesis of inflammatory markers [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Specifically, IR is associated with reduced nitric oxide (NO) production by endothelial cells and increased release of procoagulant factors. These alterations contribute to vasoconstriction, platelet activation and aggregation, as well as endothelial dysfunction, all of which play a pivotal role in the pathogenesis of atherosclerosis and thrombosis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, IR can further aggravate vascular damage by inducing smooth muscle cell proliferation and the release of pro-inflammatory factors [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, greater stroke severity is associated with higher levels of IR and more pronounced inflammatory response [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Given that IR plays a pivotal role in the development of diabetes, we further conducted a subgroup analysis according to diabetes status to assess whether blood glucose levels affect the aforementioned effects and mechanisms. The results indicate that a higher TyG index is more strongly associated with an increased risk of strokein the group without diabetes than in person with diabetes. Specifically, among the group without diabetes, those with a high TyG index had a significantly increased risk of stroke, with the risk being elevated by approximately 60% to 93%. A prospective study of 17,708 persons without diabetes individuals revealed that, compared with those in the lowest quartile of the TyG index, individuals in the other quartiles had a 64%\u0026ndash;74% higher risk of stroke\u0026mdash;an increase that closely aligns with the 60%\u0026ndash;93% elevation observed in our study [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, yang et al. 's research found that there was no significant association between the TyG index and the recurrence and prognosis of stroke. The area under the ROC curve of the TyG index was only 0.56, indicating its limited value in predicting stroke recurrence and mortality among the group without diabetes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A study based on the association between different TyG-related indices under different glucose statuses and the risk of stroke found that the triglyceride-glucose body mass index (TYG-BMI) and triglyceride-glucose waist circumference (TYG-WC) indices had significant predictive capabilities in the group without diabetes, while the TyG index had better predictability in person with diabetes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This emphasizes the importance of identifying appropriate predictive biomarkers that are tailored to individuals with distinct glucose statuses, thereby facilitating more accurate and individualized clinical decision-making by clinicians for patients at different stages of diabetes.\u003c/p\u003e \u003cp\u003eCystatin C is a non-glycosylated, basic protein secreted by all nucleated cells and metabolized primarily in the proximal renal tubules. It serves as a reliable biomarker for evaluating renal function [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. More and more research evidence has revealed that cystatin C also shows great potential in assessing cardiovascular events [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A meta-analysis of 14 studies from 8 countries, including China, demonstrated that individuals with the highest cystatin C levels had a 162% higher risk of CVD and an 83% higher risk of stroke compared with those in the lowest tertile. Notably, the association with fatal CVD was even more pronounced [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Similarly, our study found that the risk of stroke was significantly 26% higher in the high cystatin C group compared with the control group, lower than 83% in the meta-study, which might be related to the difference in population distribution. A Mendelian randomization analysis of 16 prospective cohorts revealed no evidence of a causal association between cystatin C levels and the risk of stroke, implying that cystatin C is unlikely to be a causative factor in stroke pathogenesis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Another genetic study, utilizing data from the UK Biobank, identified loci associated with cystatin C. Through Mendelian randomization analysis, the study confirmed that cystatin C exerts a promoting effect on stroke, thereby providing genetic-level evidence for an association between cystatin C and stroke risk [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The aforementioned research is controversial regarding the existence of a causal relationship between cystatin C and stroke. However, it is evident that cystatin C, as a biomarker, exhibits predictive value for stroke risk. Nevertheless, further research is required to elucidate the underlying mechanisms linking them. The influence of high cystatin C on the occurrence of stroke may be related to its regulation of inflammatory processes, involvement in atherosclerosis development, effects on vascular wall remodeling, and potential direct cytotoxic actions, etc. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. On the other hand, some studies propose that cystatin C plays a pivotal role in neuroprotection induced by hyperbaric oxygen (HBO) preconditioning, suggesting that there may be a bidirectional effect between cystatin C and stroke [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The results of the restricted cubic spline analysis indicated nonlinear associations between both the TyG index and Cystatin C and the risk of stroke, providing further support for this hypothesis. Specifically, the CATIS study found a negative correlation between cystatin C levels and post-stroke cognitive impairment [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], whereas the Health ABC study showed a positive association between high cystatin C levels and cognitive dysfunction in older adults [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Currently, there is some controversy over the neuroprotective effect of cystatin C in clinical evidence. The results of the aforementioned studies suggest that cystatin C may serve as a reliable biomarker for stroke prediction. However, its utility as an interventional target for reducing the risk of stroke and improving clinical outcomes remains limited, and further evidence is required to establish its therapeutic potential [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Subgroup analysis stratified by the presence or absence of heart disease revealed that, compared with participants without heart disease, those with heart disease exhibited a significantly greater increase in stroke risk with elevated cystatin C levels. Collectively, the existing evidence underscores that cystatin C serves not only as a marker of renal function but also exhibits a robust correlation with cardiovascular health [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, no previous studies have investigated the predictive value of the TyG index combined with cystatin C for stroke in the Chinese population. Therefore, this study aims to evaluate the long-term predictive value of the TyG index combined with cystatin C for stroke risk in the Chinese population aged 45 years and older without kidney disease. Our findings indicate that the combination of the TyG index and cystatin C demonstrates superior predictive value for stroke occurrence compared to either marker alone. Lee et al. demonstrated that IR and cystatin C synergistically contribute to the development and progression of CVD in patients with type 2 diabetes by jointly mediating inflammatory pathways [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In the mediation analysis, a significant interaction was observed between the TyG index and cystatin C. Notably, the mediating effect between the two was non-positive, indicating a potentially complex regulatory mechanism, which may involve metabolic compensation. Although the results indicate a potential inhibitory interaction between the two factors, the direct effect remains dominant, thereby obscuring the mediating effect\u0026rsquo;s negative contribution. Nonetheless, stroke risk exhibits the most pronounced increase when both factors are simultaneously elevated. In summary, the combined use of the TyG index and cystatin C markedly improves the predictive ability for stroke. Both the TyG index and cystatin C are non-invasive biomarkers that are readily accessible and widely utilized in clinical settings. Their integration into routine clinical practice holds promise as a novel strategy for the early identification and risk stratification of individuals at high risk of stroke, thereby facilitating timely preventive interventions. Additional research is warranted to clarify the potential roles and mechanistic pathways of the TyG index and cystatin C in stroke development.\u003c/p\u003e \u003cp\u003eWhile this study yields meaningful findings, some limitations merit further consideration. First, as an observational study, the findings may be influenced by residual confounding factors, such as psychological characteristics and household income. Consequently, it is not possible to establish a causal relationship between the TyG index, cystatin C, and stroke. Second, the standard cutoff values for the TyG index have not yet been universally established. In the current analysis, median stratification was employed, which may not accurately reflect clinically relevant threshold values. Third, stroke outcomes were determined solely based on self-reported physician diagnoses, which may be subject to recall bias. Fourth, patients with kidney disease were excluded from the study population, limiting the generalizability of the findings to the general population, which warrants further investigation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBoth elevated TyG index and elevated serum cystatin C levels were independently associated with a significantly higher risk of stroke in individuals without renal disease. The concurrent use of these biomarkers conferred improved predictive accuracy for stroke events. Interestingly, a significant negative interaction was observed between the TyG index and cystatin C; however, the pathophysiological basis of this interaction remains uncertain and merits further mechanistic inquiry.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHARLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChina Health and Retirement Longitudinal Study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHBO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHyperbaric oxygen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHOMA-IR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHomeostasis model assessment for insulin resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsulin resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNitric oxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTyG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglyceride-glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTyG-BMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglyceride-glucose body mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTyG-WC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etriglyceride-glucose waist circumference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHARLS study received ethical approval from the Peking University Institutional Review Board (IRB00001052-11015).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent prior to their involvement in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available in the CHARLS repository, https://charls.pku.edu.cn\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChunTing Zhou :\u0026nbsp;\u003c/strong\u003eConceptualization, Writing - Review \u0026amp; Editing, Supervision，Project administration, \u003cstrong\u003eTian She\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eFormal analysis, Resources, Data Curation, Writing - Original Draft, Visualization,\u0026nbsp;\u003cstrong\u003eHua Li\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eConceptualization, Writing - Review \u0026amp; Editing, Supervision, Project administration\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eJinghao Rong\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eMethodology, Software, Validation Writing - Original Draft, Visualization,\u0026nbsp;\u003cstrong\u003eMin Yu:\u0026nbsp;\u003c/strong\u003eSupervision, Project administration. \u003cstrong\u003eChunTing Zhou\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eTian She\u0026nbsp;\u003c/strong\u003econtributed equally.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: \u003c/strong\u003enot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHilkens NA, Casolla B, Leung TW, de Leeuw F-E, Stroke. 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Eur J Neurol. 2021;28:1265\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorrea S, Morrow DA, Braunwald E, Davies RY, Goodrich EL, Murphy SA, et al. Cystatin C for risk stratification in patients after an acute coronary syndrome. J Am Heart Assoc. 2018;7:e009077.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Triglyceride-Glucose (TyG) Index, Cystatin C, Stroke, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-9229681/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9229681/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 and cystatin C are recognized as valuable predictors of stroke risk. However, their combined predictive value for stroke remains unclear.This study aims to assess the association between these two biomarkers and investigate their potential interaction.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cohort study included 4,483 adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years from the China Health and Retirement Longitudinal Study, with up to 9 years of follow-up for incident stroke. Participants were grouped by TyG index (cutoff: 8.59) and cystatin C level (cutoff: 1.06 mg/L), with Cox models evaluating stroke risk differences across groups. Survival probabilities were estimated using the Kaplan\u0026ndash;Meier method. Mediation analysis explored the interplay between the two biomarkers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 4,483 participants, 347 (7.7%) developed stroke over a 9-year follow-up period. Compared with controls, stroke risk was elevated in the high TyG index (HR\u0026thinsp;=\u0026thinsp;1.29, 1.03\u0026ndash;1.61), high cystatin C (HR\u0026thinsp;=\u0026thinsp;1.26, 1.00-1.60), and especially the combined high-risk group (HR\u0026thinsp;=\u0026thinsp;1.66, 1.18\u0026ndash;2.33). Furthermore, mediation analysis suggested a significant mediating relationship between the TyG index and cystatin C.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eBoth the TyG index and cystatin C are strongly associated with the incidence of stroke. Their combined application enhances the predictive power for stroke events, with evidence of a significant mediating effect between them.\u003c/p\u003e","manuscriptTitle":"Combined Associations of Triglyceride-Glucose Index (TyG) and Cystatin C with Stroke Incidence: A National Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 08:36:25","doi":"10.21203/rs.3.rs-9229681/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"125480323507473542261987087258767605023","date":"2026-04-29T11:26:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T15:02:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68900297480461446868298983171901923258","date":"2026-04-22T03:28:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-22T03:26:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-31T06:44:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-30T09:09:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-30T09:09:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-03-26T05:49:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"162067f9-ebad-4411-a946-1d9ed29f02af","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"125480323507473542261987087258767605023","date":"2026-04-29T11:26:46+00:00","index":46,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T08:36:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 08:36:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9229681","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9229681","identity":"rs-9229681","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}