Skip to main content
Download PDF

The relative and combined ability of triglyceride-glucose index and stress hyperglycemia ratio to predict major adverse cardio-cerebral events in patients with multivessel coronary artery disease

Diabetology & Metabolic Syndrome volume 16, Article number: 234 (2024) Cite this article

Abstract

Background

Cardiovascular disease continues to be the leading cause of global mortality and disability, particularly posing elevated risks in patients diagnosed with multivessel disease (MVD). Efficient risk stratification in MVD patients is crucial for improving prognosis, prompting investigation into novel biomarkers such as the triglyceride-glucose index (TyG index) and the stress hyperglycemia ratio (SHR).

Methods

This study enrolled a cohort comprising 679 patients diagnosed with MVD who underwent coronary angiography at Tianjin Chest Hospital. Patients were stratified into four groups based on their TyG index levels, categorized as TyG index-L and TyG index-H, and SHR levels, categorized as SHR-L and SHR-H. The primary endpoint was the occurrence of major adverse cardio-cerebral events (MACCEs). This Study conducted univariate and multivariable Cox regression analyses to assess the association between TyG index and SHR levels, both as continuous and categorical variables, in relation to MACCEs. Kaplan–Meier survival curves were employed to evaluate the correlation among patient groups.

Results

During a mean follow-up of 61 months, 153 cases of MACCEs were recorded. The TyG index and SHR served as independent predictors of long-term prognosis in patients with MVD, whether considered as continuous or categorical variables. Multivariable analysis revealed that patients with TyG index-H + SHR-H group exhibited the highest incidence of MACCEs (HR: 2.227; 95% CI 1.295–3.831; P = 0.004). The area under the curve (AUC) for predicting MACCEs was 0.655 for TyG index, 0.647 for SHR, and 0.674 when combined.

Conclusion

This study underscores the potential of the TyG index and SHR as independent and combined predictive markers for MACCEs in patients with MVD. Their integrated assessment enhances risk stratification, providing valuable insights for personalized treatment strategies aimed at optimizing patient prognosis.

Introduction

Cardiovascular disease remains a leading global health issue, representing the foremost cause of both mortality and disability [1,2,3]. Among these conditions, coronary artery disease (CAD) exhibits significantly higher rates of both mortality and incidence [4]. Patients with multivessel disease (MVD) encounter substantially increased risks of heart failure, complications, and major adverse cardio-cerebral events (MACCEs) relative to individuals with single-vessel CAD [5,6,7]. Consequently, devising revascularization strategies and pharmacological interventions for MVD requires careful and nuanced consideration [8, 9]. Early and effective risk stratification can markedly enhance both long-term and short-term outcomes by mitigating mortality and the incidence of MACCEs during follow-up [10]. To optimize patient prognosis, exploring supplementary management strategies and methodologies is essential [11]. Therefore, early and precise risk stratification is critical for patients with MVD, particularly for forecasting future MACCE outcomes.

The triglyceride-glucose index (TyG index) has emerged as a significant biomarker of insulin resistance and metabolic dysfunction in contemporary research. The TyG index combines levels of fasting triglycerides (TG) and fasting plasma glucose (FPG), serving as a potential alternative marker for insulin resistance (IR) and providing predictive insights into adverse outcomes [12, 13]. Given its accessibility and cost-effectiveness, TyG index is increasingly acknowledged as a valuable tool for both diagnostic and prognostic assessment [14]. Recent studies consistently reveal robust associations between the TyG index and elevated cardiovascular event risk across both general populations and individuals with cardiovascular disease [15, 16], notably affecting prognosis in patients with MVD [17, 18].

The stress hyperglycemia ratio (SHR) has become a significant research biomarker in recent years, derived from admission blood glucose (ABG) and glycated hemoglobin (HbA1c) levels [19]. Compared to traditional ABG measurements, SHR provides a more objective marker of hyperglycemia, overcoming limitations associated with quantifying stress-induced hyperglycemia [20], thus enhancing its clinical applicability. Numerous studies in recent years have identified a significant association between SHR in patients with CAD and adverse outcomes [21,22,23]. Furthermore, recent research has revealed a substantial correlation between SHR and MVD in CAD patients, emphasizing its predictive value for adverse outcomes [24].

While previous studies have explored the association between the TyG index, SHR, and cardiovascular risk, the majority have concentrated on patients with chronic total occlusion (CTO) [25]. However, the interplay between TyG index and SHR levels in predicting MACCEs among MVD patients remains poorly understood. Current research predominantly addresses these markers independently for prognostic risk stratification. Given their ubiquitous availability in clinical settings and their correlation with adverse outcomes in MVD patients, elucidating the individual and combined significance of these markers is imperative. Thus, this study aims to investigate the individual and synergistic prognostic implications of TyG index and SHR levels in MVD patients.

Method

Study population

This retrospective observational cohort study focused on patients presenting with MVD (defined as an angiographic diameter stenosis of ≥ 50% in at least two major epicardial coronary arteries, with or without involvement of the left main artery). A total of 1203 consecutive patients who underwent coronary angiography (CAG) due to chest pain between January 2017 and December 2018 at Tianjin Chest Hospital were initially included. Exclusion criteria comprised: (1) missing data on FPG or HbA1c (n = 179); (2) age > 85 years at admission (n = 54); (3) severe valvular or congenital heart disease (n = 67); (4) severe hepatic dysfunction, acute infection, malignancy, or severe kidney dysfunction (n = 32); (5) lack of CAG data (n = 18); (6) absence of follow-up data (n = 174). Ultimately, 679 patients were enrolled in the study (Fig. 1). All enrolled patients completed clinical follow-up through telephone interviews or outpatient visits between November 2022 and March 2023. The primary endpoint assessed was MACCEs (defined as non-fatal myocardial infarction, coronary and cerebrovascular revascularization, unstable angina or rehospitalization for heart failure, cardiac death, non-fatal stroke and brain-derived death). Based on the median TyG index, patients were categorized into two groups: TyG index-L group (< 8.95, n = 339) and TyG index-H group (≥ 8.95, n = 340). Similarly, based on the median fasting SHR, patients were divided into SHR-L group (< 0.86, n = 340) and SHR-H group (≥ 0.86, n = 339). Further stratification was performed based on both TyG index and fasting SHR levels, resulting in four groups: TyG-L + SHR-L group (n = 226), TyG-H + SHR-L group (n = 114), TyG-L + SHR-H group (n = 113), and TyG-H + SHR-H group (n = 226). This study received approval from the Ethics Committee of Tianjin Chest Hospital and was conducted in accordance with the principles outlined in the Declaration of Helsinki. Given its retrospective design, individual informed consent from patients was not sought.

Fig. 1
figure 1

Patients flowchart

Full size image

Data collection and definitions

Baseline clinical and laboratory data were systematically gathered by trained investigators from electronic medical records, ensuring blinding to the study’s objectives. Clinical data encompassed demographic information such as age and gender, along with details on diabetes duration, hypertension history, clinical presentation, left atrial volume (LA), left ventricular volume (LV), left ventricular ejection fraction (LVEF), target vessels, and discharge medications including clopidogrel, ticagrelor and statins. Laboratory analyses included a comprehensive array of parameters: high-sensitivity cardiac troponin (hs-Tn), creatine kinase isoenzymes (CK-MB), international normalized ratio (INR), uric acid (UA), total cholesterol (TC), TG, low-density lipoprotein (LDL), high-density lipoprotein (HDL), lipoprotein (a) [Lp(a)], homocysteine (HCY), FPG, HbA1c, high-sensitivity C-reactive protein (hs-CRP), N-terminal proB-type natriuretic peptide (NT-proBNP), and serum creatinine levels. The TyG index was computed using the formula Ln (fasting TGs [mg/dL] × FPG [mg/dL]/2), while the SHR was defined as [(first FPG (mmol/l))/(1.59 × HbA1c (%) − 2.59)].

Statistical analysis

Continuous variables were reported as me an ± standard deviation if normally distributed; otherwise, they were presented as median with interquartile range. Differences between continuous variables were assessed using Student’s t-test or the Mann–Whitney U test. Categorical variables were expressed as frequencies and percentages and were analyzed using the chi-square test or Fisher’s exact test. Event-free survival rates between two groups were evaluated using the Kaplan–Meier method and log-rank test. Univariate Cox regression analysis identified independent predictors of MACCEs, including age, male gender, diabetes, CK-MB, UA, TC, TG, ALB, NT-proBNP, hs-CRP, and statin use. Multivariable Cox proportional hazards regression analysis was conducted to explore the relationship between individual biomarkers (both continuous and categorical variables) and MACCEs. Receiver operating characteristic (ROC) curves were used to determine optimal cut-off values for predicting MACCEs using the TyG index and SHR. Nonlinear associations among TyG index, SHR and MACCEs were examined using restricted cubic spline (RCS) analysis. All study data were analyzed using SPSS (version 25.0, IBM, Chicago) and R (version 4.2.0, R) statistical software. P < 0.05 was considered statistically significant.

Results

Baseline characteristics of patients

Baseline patient characteristics are summarized in Table 1. During a mean follow-up period of 61 months, 153 patients (22.5%) experienced MACCEs. Of the 679 patients studied, 76.1% were male with a mean age of 62 ± 11 years. Patients who experienced MACCEs were significantly older (P = 0.002), predominantly male (P = 0.041), and exhibited a higher prevalence of diabetes (P < 0.001) compared to those who did not experience MACCEs. Furthermore, the MACCEs cohort displayed elevated levels of systolic blood pressure (SBP) (P = 0.035), hs-Tn (P = 0.010), CK-MB (P = 0.034), serum creatinine (P = 0.007), UA (P = 0.012), TC (P = 0.032), TG (P = 0.003), NT-proBNP (P < 0.001), FBG (P < 0.001), hs-CRP (P = 0.040), TyG index (P < 0.001), and SHR (P < 0.001). Moreover, the MACCEs group exhibited lower LVEF (P = 0.031), ALB (P = 0.007), and utilization of statins at discharge (P < 0.001) compared to the non-MACCEs group. No statistically significant differences were observed between the groups in terms of hypertension prevalence, disease time, LA, LV, INR, HDL-C, LDL-C, Lp(a), TP, HCY, HbA1c, number of target vessels, or medications other than statins at discharge (P > 0.05).

Table 1 Baseline characteristics of the study population
Full size table

Associations of TyG index and MACCEs

Univariate and multivariate Cox proportional hazards regression analyses are presented in Table 2. In the univariate analysis, variables associated with MACCEs include age, male gender, diabetes, CK-MB, creatinine, UA, TC, TG, ALB, NT-proBNP, hs-CRP, and statin use (S1, S2). Multivariable Cox proportional hazards regression analyses incorporating both TyG index and SHR demonstrate that TyG index (HR 3.049; 95% CI 1.974–4.708; P < 0.001) and SHR (HR 1.631; 95% CI 1.974–4.708; P < 0.001) independently predict MACCEs. Patients were stratified into two groups based on the median TyG index level (TyG-L group: < 8.95, n = 339; TyG-H group: ≥ 8.95, n = 340), with MACCEs rates of 14.7% and 30.2%, respectively (P < 0.001). Kaplan–Meier survival analysis depicted in Fig. 2a shows a significantly higher cumulative incidence of MACCEs with increasing TyG index values (Log rank P < 0.001). Adjusted multivariable Cox regression analysis, including age, male gender, diabetes, CK-MB, creatinine, UA, TC, TG, ALB, NT-proBNP, hs-CRP, and statin use, reveals a heightened risk of MACCEs in the high TyG index group compared to the low TyG index group (HR 1.586; 95% CI 1.024–2.455; P = 0.039). RCS curves adjusted for these variables demonstrate a positive linear relationship between TyG index and MACCEs risk (P < 0.001, Fig. 3a). Schoenfeld residual test was performed again after the adjustment (P > 0.05), which showed that the modified measures effectively improved the compliance of the proportional risk hypothesis. ROC analysis for MACCEs prediction identifies the optimal TyG index cut-off as 8.99 (sensitivity: 62.6%, specificity: 66.0%), with an area under the curve (AUC) of 0.655 (95% CI 0.606–0.704, P < 0.001) (Table 3, Fig. 4).

Table 2 Associations of TyG index and SHR with MACCEs
Full size table
Fig. 2
figure 2

The event-free survival rate in TyG index a, SHR b, and combined groups c. TyG triglyceride-glucose, SHR stress hyperglycemia ratio, MACCEs major adverse cardio-cerebral events

Full size image
Fig. 3
figure 3

Restricted cubic spline curves for the association of TyG index a and SHR b with the risk of all-cause mortality in the adjusted model. TyG triglyceride-glucose, SHR stress hyperglycemia ratio.adjusted for age, male, diabetes, CK-MB, creatinine, UA, TC, TG, ALB, NT-proBNP, hs-CRP, statins. SHR stress hyperglycemia ratio, TyG triglyceride-glucose

Full size image
Table 3 ROC Curve Analysis of SHR and TyG index
Full size table
Fig. 4
figure 4

ROC curve for TyG index, SHR and their combination in predicting MACCEs. Optimal cut-off: TyG Index: 8.99; SHR: 0.86. AUC TyG index: 0.655 (0.606–0.704), AUC SHR: 0.647 (0.595–0.699); AUC combine: 0.674 (0.625–0.723). ROC receiver operating characteristic, AUC an area under the curve, TyG triglyceride-glucose, SHR stress hyperglycemia ratio

Full size image

Associations of SHR and MACCEs

Similarly, patients were stratified into two groups based on the median value of SHR (SHR-L group: < 0.86, n = 340; SHR-H group: ≥ 0.86, n = 339). Rates of MACCEs were 14.1% and 31.0% in the SHR-L and SHR-H groups, respectively (P < 0.001) (Table 2). Kaplan–Meier survival analysis (Fig. 2b) revealed a significant increase in MACCEs with higher SHR levels (Log Rank P < 0.001). When analyzed as a categorical variable, the adjusted hazard ratio for higher SHR levels was 1.761 (95% CI 1.175 to 2.639; P = 0.006). Restricted cubic spline curves demonstrated a positive, linear relationship between SHR and MACCE risk (P < 0.001) (Fig. 3b). Schoenfeld residual test was performed again after the adjustment (P > 0.05), which showed that the modified measures effectively improved the compliance of the proportional risk hypothesis. ROC analysis determined that 0.86 was the optimal threshold for predicting MACCEs, with a sensitivity of 59.0% and specificity of 70.6%. The AUC was calculated as 0.647 (95% CI: 0.595–0.699, P < 0.001) (Table 3, Fig. 4).

Inter‑relationship of TyG index, SHR and MACCEs

Analysis revealed a significant positive correlation between TyG index and SHR (R = 0.507, P < 0.001) (Fig. 5). To explore the relationship between TyG index, SHR, and MACCEs, patients were categorized into four groups: TyG-L + SHR-L (n = 226), TyG-H + SHR-L (n = 114), TyG-L + SHR-H (n = 113), and TyG-H + SHR-H (n = 226). The respective MACCEs rates for these groups were 11.9%, 18.4%, 20.4%, and 36.3% (Table 2). Compared to the TyG-L + SHR-L group, the TyG-L + SHR-H and TyG-H + SHR-H groups had 1.781-fold and 3.629-fold higher MACCEs rates, respectively. After adjusting for potential confounders, the TyG-H + SHR-H group exhibited a 2.227-fold higher risk of MACCEs compared to the reference group (HR: 2.227 95%CI 1.295–3.831, P = 0.004) (Table 2). Schoenfeld residual test was performed again after the adjustment (P > 0.05), which showed that the modified measures effectively improved the compliance of the proportional risk hypothesis. Kaplan–Meier survival analysis (Fig. 2c) demonstrated that the MACCEs rate was highest in the TyG-H + SHR-H group among the four groups (Log rank P < 0.001). The AUC was calculated as of 0.674 (95% CI 0.625–0.723, P < 0.001) (Table 3, Fig. 4).

Fig. 5
figure 5

Scatter diagram showing the correlation between SHR and the TyG index (R = 0.507, P < 0.001)

Full size image

Discussion

This study represents the first comprehensive investigation into patients with MVD, examining the relationship between the TyG index and the SHR in predicting MACCEs. Key findings are as follows: Firstly, both the TyG index and SHR demonstrated a significant positive correlation with increased risk of MACCEs. Each marker independently forecasts MACCEs. Secondly, the combined use of these indices markedly improved the prediction of MACCEs and thus provided a more effective risk stratification compared to using the TyG index or SHR alone. Patients with both high TyG index and high SHR demonstrated a 2.227-fold increased risk of MACCEs compared to those with both low TyG index and low SHR. Thirdly, the integrated utilization of TyG index and SHR yielded more nuanced prognostic insights. Crucially, the concurrent utilization of the TyG index and SHR holds considerable promise for advancing risk stratification in patients with MVD.

In this study, the TyG index-H + SHR-L cohort did not exhibit statistical significance (P = 0.137). Conversely, the TyG index-L + SHR-H cohort demonstrated statistical significance (P = 0.042), although this significance diminished after adjusting for variables (P = 0.402). Our retrospective analysis suggests that the SHR grouping standard may be more effective than the TyG index-based grouping. This conclusion is supported by the observation that the optimal cutoff value for SHR, determined from the ROC curve (0.86), aligns with the selected grouping threshold. In contrast, the cutoff value for the TyG index (8.95) is lower than the ROC-derived threshold (8.99), leading to suboptimal grouping and consequent statistical discrepancies. This analysis provides a mutual validation of the two approaches, though limitations in sample size should be noted. Future research should focus on increasing sample sizes and carefully refining cutoff values to further validate these findings.

CAD currently stands out due to its significant risk and burden, particularly with MVD patients facing heightened risks [1,2,3,4,5,6,7]. Therefore, research efforts have focused on elucidating the pathogenesis, prediction, and risk assessment in MVD patients. In this context, an expanding array of novel indicators, such as the TyG index and SHR, are under consideration for potential application in MVD patients [17, 18, 24].

The TyG index has emerged as a widely investigated metabolic marker in recent years, closely associated with cardiovascular diseases due to its cost-effectiveness and accessibility [14]. IR is a pivotal pathogenic factor that elevates thrombotic, inflammatory markers, and reactive oxygen species (ROS), thereby fostering the formation of atherosclerotic plaques [26, 27]. The TyG index has been validated as a reliable surrogate marker for IR through studies utilizing hyperinsulinemic-euglycemic clamp (HIEC) and homeostasis model assessment of insulin resistance (HOMA-IR) [28,29,30]. Research on the TyG index has primarily centered on its cardiovascular implications, linking it with adverse outcomes such as acute coronary syndrome (ACS) [16, 31], CTO [32] and early-onset coronary artery disease [33]. Studies encompass diverse populations, including both diabetic and non-diabetic individuals [17, 34]. In a large-scale Asian study, the TyG index across all states of glucose metabolism exhibited a positive correlation with the severity of CAD, consistent with the findings that linearly associate it with MACCEs, suggesting its potential as a non-invasive predictor of CAD severity [18]. Moreover, it consistently emerged as an independent risk factor for MACCEs in both continuous and categorical analyses. Studies in patients with MVD have identified independent and combined associations between the TyG index, NT-proBNP, and the risk of adverse cardiovascular events [17], aligning with similar findings in current research. Consequently, its combined utilization with other markers remains pivotal in ongoing investigations.

The SHR, as a novel biomarker, is increasingly supported by evidence linking higher levels with significantly elevated short-term and long-term adverse outcomes in cardiovascular disease patients. Similar to the TyG index, research has encompassed patients with ACS [21, 23, 35], CTO [25], heart failure (HF) [36], and nonobstructive coronary arteries (INOCA) [37]. Traditional ABG measurements typically provide a snapshot of glycemic control. However, these admission glucose measurements may not fully capture the dynamic alterations in glucose metabolism that occur during acute stress or critical illness. In contrast, the SHR offers a more nuanced evaluation by comparing stress-induced hyperglycemia to baseline glucose levels. Compared to traditional ABG measurements, SHR provides a more objective measure of hyperglycemia, addressing limitations in quantifying stress-induced hyperglycemia [20] and is recommended for accurately reflecting background glucose metabolism status [19]. In studies related to MVD, a large cohort study demonstrated a significant association between SHR and adverse prognosis in CAD patients, suggesting its potential as a predictive marker for adverse outcomes [38], consistent with current findings. Similar to the combined use of TyG index and NT-proBNP, studies by Wang, et al. [24] also investigated the individual and combined predictive roles of SHR and NT-proBNP in MVD patients, showing that combining SHR and NT-proBNP levels provides enhanced prognostic information, aligning with current study results. This indirectly corroborates findings regarding the study of TyG index and SHR in research. These observations underscore the potential clinical utility of SHR alongside established markers like TyG index in predicting adverse cardiovascular outcomes, highlighting their complementary roles in risk assessment and management.

A substantial body of literature currently advocates for combined biomarker analysis to enhance prognostication and risk stratification in patients with MVD [17, 24]. The TyG index has shown considerable promise in recent cardiovascular research, primarily focusing on its association with patient glucose metabolism [16,17,18, 34]. Concurrently, the SHR offers a more objective measure among current glucose metabolism markers [19, 20], and has been extensively studied in the context of MVD [24]. Therefore, evaluating both the TyG index and SHR as independent and combined prognostic biomarkers holds crucial clinical significance for risk stratification in MVD patients. In clinical practice, the measurement of FPG, HbA1, and TG levels via blood tests is both feasible and cost-effective. Hence, concurrent assessment of SHR and the TyG index for future risk stratification of major adverse cardiac and MACCEs in MVD patients is both practical and clinically meaningful. These observations underscore the importance of integrating novel biomarkers such as the TyG index and SHR into clinical practice to enhance risk assessment and management strategies for patients with complex cardiovascular conditions like MVD.

A large-scale prospective cohort study highlighted significant correlations between elevated TyG index and SHR levels with adverse outcomes in CTO patients, underscoring their reliability in predicting long-term prognosis [25]. While the study in question employed a larger sample size and encompassed multiple outcome measures, thus offering a comprehensive risk assessment for patients with CTO, its broader focus may constrain its applicability to more specific patient populations, such as those with MVD. The findings of this study underscore the critical role of integrating the TyG index and SHR for enhancing predictive accuracy in MVD patients. Future research should investigate the application of these biomarkers across various cardiovascular conditions and seek strategies to further refine their predictive capabilities for more precise, individualized treatment. Conversely, although the extensive evaluation provided by the broader study offers valuable insights for general cardiovascular risk management, its relevance to specific patient groups warrants further validation.

So far, this study represents the inaugural exploration into the combined prognostic significance of TyG index and SHR levels in patients diagnosed with MVD. Nonetheless, the underlying pathophysiological mechanisms associating TyG index and SHR levels independently and in concert with MACCEs in MVD patients remain indeterminate. On one hand, TyG index is principally regarded as indicative of IR and may additionally reflect current inflammatory elevation [26, 27]. Conversely, SHR serves as a more objective indicator of glucose metabolism response [20] and potentially reflects current inflammatory status [39], suggesting a plausible mechanism for their synergistic effects. On the other hand, lipid and glucose metabolism are generally more severe and prolonged in most MVD patients compared to those with single-vessel CAD, likely attributable to the close correlation between atherosclerotic plaque formation and these conditions [40]. Thus, these two markers could interact through such metabolic pathways. It is essential to highlight that study exclusively involved patients with MVD lesions, thereby precluding confirmation of the prognostic and predictive advantages of combining TyG index and SHR in other cardiovascular disease populations. Future investigations should delve deeper into these potential mechanisms to elucidate how TyG index and SHR may synergistically impact outcomes in MVD patients and explore their applicability across broader spectra of cardiovascular diseases.

Limitation

This investigation is subject to several limitations. Firstly, its single-center design and exclusive focus on a Chinese cohort constrain the generalizability of findings to broader populations. Secondly, the sample size is insufficient compared to larger, multicenter studies, necessitating further expansion in future research endeavors. Thirdly, this study did not elucidate the dynamic changes in TyG index and SHR during follow-up, thereby hindering assessment of these biomarkers’ prognostic implications, particularly after discharge. Fourthly, although this study adjusted for potential confounders in multivariable regression model, residual confounding factors that were not measured cannot be entirely excluded. Fifthly, notably, this study did not incorporate follow-up assessments of quality of life. Lastly, due to the observational nature of the study, residual or unmeasured confounding factors may still affect results. Further prospective cohort studies and mechanistic investigations are needed to substantiate findings.

Conclusion

This study presents preliminary evidence indicating that elevated TyG index and SHR levels independently predict the diagnosis of MACCEs in patients with MVD. The synergistic relationship between these biomarkers suggests that individuals with higher TyG index and SHR levels face a substantially heightened risk of MACCEs. Both biomarkers offer potential as tools for identifying high-risk patients and directing subsequent treatment or follow-up strategies. Their integrated use has the potential to refine risk stratification in clinical practice, facilitating more targeted interventions aimed at improving outcomes for patients with MVD.

Availability of data and materials

The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

References

  1. Vaduganathan M, Mensah GA, Turco JV, Fuster V, Roth GA. The global burden of cardiovascular diseases and risk: a compass for future health. J Am Coll Cardiol. 2022;80(25):2361–71. https://doi.org/10.1016/j.jacc.2022.11.005. (Epub 2022 Nov 9 PMID: 36368511).

    Article  PubMed  Google Scholar 

  2. Correction to: 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation. 2024 May 7; 149(19):e1164. https://doi.org/10.1161/CIR.0000000000001247. Epub 2024 May 6. Erratum for: Circulation. 2024 Feb 20; 149(8):e347-e913. https://doi.org/10.1161/CIR.0000000000001209. PMID: 38709844.

  3. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP, Bonny A, Brauer M, Brodmann M, Cahill TJ, Carapetis J, Catapano AL, Chugh SS, Cooper LT, Coresh J, Criqui M, DeCleene N, Eagle KA, Emmons-Bell S, Feigin VL, Fernández-Solà J, Fowkes G, Gakidou E, Grundy SM, He FJ, Howard G, Hu F, Inker L, Karthikeyan G, Kassebaum N, Koroshetz W, Lavie C, Lloyd-Jones D, Lu HS, Mirijello A, Temesgen AM, Mokdad A, Moran AE, Muntner P, Narula J, Neal B, Ntsekhe M, Moraes de Oliveira G, Otto C, Owolabi M, Pratt M, Rajagopalan S, Reitsma M, Ribeiro ALP, Rigotti N, Rodgers A, Sable C, Shakil S, Sliwa-Hahnle K, Stark B, Sundström J, Timpel P, Tleyjeh IM, Valgimigli M, Vos T, Whelton PK, Yacoub M, Zuhlke L, Murray C, Fuster V; GBD-NHLBI-JACC Global Burden of Cardiovascular Diseases Writing Group. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020; 76(25):2982–3021. https://doi.org/10.1016/j.jacc.2020.11.010. Erratum in: J Am Coll Cardiol. 2021 Apr 20; 77(15):1958–1959. PMID: 33309175; PMCID: PMC7755038.

  4. Hao Y, Liu J, Liu J, Yang N, Smith SC Jr, Huo Y, Fonarow GC, Ge J, Taubert KA, Morgan L, Zhou M, Xing Y, Ma CS, Han Y, Zhao D. Sex differences in in-hospital management and outcomes of patients with acute coronary syndrome. Circulation. 2019;139(15):1776–85. https://doi.org/10.1161/CIRCULATIONAHA.118.037655. (PMID: 30667281).

    Article  PubMed  Google Scholar 

  5. Sorajja P, Gersh BJ, Cox DA, McLaughlin MG, Zimetbaum P, Costantini C, et al. Impact of multivessel disease on reperfusion success and clinical outcomes in patients undergoing primary percutaneous coronary intervention for acute myocardial infarction. Eur Heart J. 2007;28(14):1709–16.

    Article  PubMed  Google Scholar 

  6. Yim S, Ahn JH, Jeong MH, Ahn Y, Kim JH, Hong YJ, et al. Impact of sex difference on clinical outcomes in acute myocardial infarction patients with single-vessel and multi-vessel disease: based on Korea acute myocardial infarction registry-national institute of health. Cardiovasc Diagnosis Therapy. 2023;13(4):660–72.

    Article  Google Scholar 

  7. Toma A, Stähli BE, Gick M, Gebhard C, Nührenberg T, Mashayekhi K, et al. Impact of multi-vessel versus single-vessel disease on outcomes after percutaneous coronary interventions for chronic total occlusions. Clin Res Cardiol. 2017;106(6):428–35.

    Article  PubMed  Google Scholar 

  8. Zhang F, Yang Y, Hu D, Lei H, Wang Y. Percutaneous coronary intervention (PCI) versus coronary artery bypass grafting (CABG) in the treatment of diabetic patients with multi-vessel coronary disease: a meta-analysis. Diabetes Res Clin Pract. 2012;97(2):178–84.

    Article  PubMed  Google Scholar 

  9. Lim JY, Deo SV, Kim WS, Altarabsheh SE, Erwin PJ, Park SJ. Drug-eluting stents versus coronary artery bypass grafting in diabetic patients with multi-vessel disease: a meta-analysis. Heart Lung Circ. 2014;23(8):717–25.

    Article  PubMed  Google Scholar 

  10. Oncel RC, Ucar M, Karakas MS, Akdemir B, Yanikoglu A, Gulcan AR, Altekin RE, Demir I. Relation of neutrophil-to-lymphocyte ratio with GRACE risk score to in-hospital cardiac events in patients with ST-segment elevated myocardial infarction. Clin Appl Thromb Hemost. 2015;21(4):383–8. https://doi.org/10.1177/1076029613505763. (Epub 2013 Sep 27 PMID: 24078555).

    Article  PubMed  Google Scholar 

  11. Libby P, Pasterkamp G, Crea F, Jang IK. Reassessing the mechanisms of acute coronary syndromes. Circ Res. 2019;124(1):150–60. https://doi.org/10.1161/CIRCRESAHA.118.311098.PMID:30605419;PMCID:PMC6447371.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6(4):299–304.

    Article  PubMed  Google Scholar 

  13. Lopez-Jaramillo P, Gomez-Arbelaez D, Martinez-Bello D, Abat MEM, Alhabib KF, Avezum Á, Barbarash O, Chifamba J, Diaz ML, Gulec S, et al. Association of the triglyceride glucose index as a measure of insulin resistance with mortality and cardiovascular disease in populations from five continents (PURE study): a prospective cohort study. Lancet Healthy longevity. 2023;4(1):e23-33.

    Article  PubMed  Google Scholar 

  14. Wu X, Qiu W, Yang H, Chen Y-J, Liu J, Zhao G. Associations of the triglyceride-glucose index and atherogenic index of plasma with the severity of new-onset coronary artery disease in different glucose metabolic states. Cardiovasc Diabetol. 2024;23(1):76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Tian X, Zuo Y, Chen S, Liu Q, Tao B, Wu S, Wang A. Triglyceride-glucose index is associated with the risk of myocardial infarction: an 11-year prospective study in the Kailuan cohort. Cardiovasc Diabetol. 2021;20(1):19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wang L, Cong HL, Zhang JX, Hu YC, Wei A, Zhang YY, Yang H, Ren LB, Qi W, Li WY, et al. Triglyceride-glucose index predicts adverse cardiovascular events in patients with diabetes and acute coronary syndrome. Cardiovasc Diabetol. 2020;19(1):80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Wang M, Zhou L, Su W, Dang W, Li H, Chen H. Independent and joint associations between the triglyceride-glucose index and NT-proBNP with the risk of adverse cardiovascular events in patients with diabetes and acute coronary syndrome: a prospective cohort study. Cardiovasc Diabetol. 2023;22(1):149. https://doi.org/10.1186/s12933-023-01890-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhao S, Wang Z, Qing P, Li M, Liu Q, Pang X, Wang K, Gao X, Zhao J, Wu Y. Comprehensive analysis of the association between triglyceride-glucose index and coronary artery disease severity across different glucose metabolism states: a large-scale cross-sectional study from an Asian cohort. Cardiovasc Diabetol. 2024;23(1):251.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Roberts GW, Quinn SJ, Valentine N, Alhawassi T, O’Dea H, Stranks SN, Burt MG, Doogue MP. Relative hyperglycemia, a marker of critical illness: introducing the stress hyperglycemia ratio. J Clin Endocrinol Metab. 2015;100(12):4490–7.

    Article  CAS  PubMed  Google Scholar 

  20. Wexler DJ, Nathan DM, Grant RW, Regan S, Van Leuvan AL, Cagliero E. Prevalence of elevated hemoglobin A1c among patients admitted to the hospital without a diagnosis of diabetes. J Clin Endocrinol Metab. 2008;93(11):4238–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Karakasis P, Stalikas N, Patoulias D, Pamporis K, Karagiannidis E, Sagris M, Stachteas P, Bougioukas KI, Anastasiou V, Daios S, et al. Prognostic value of stress hyperglycemia ratio in patients with acute myocardial infarction: a systematic review with Bayesian and frequentist meta-analysis. Trends Cardiovasc Med. 2023. https://doi.org/10.1016/j.tcm.2023.11.006.

    Article  PubMed  Google Scholar 

  22. Cui K, Fu R, Yang J, Xu H, Yin D, Song W, Wang H, Zhu C, Feng L, Wang Z, et al. The impact of fasting stress hyperglycemia ratio, fasting plasma glucose and hemoglobin A1c on in-hospital mortality in patients with and without diabetes: findings from the China acute myocardial infarction registry. Cardiovasc Diabetol. 2023;22(1):165.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zeng G, Song Y, Zhang Z, Xu J, Liu Z, Tang X, Wang X, Chen Y, Zhang Y, Zhu P, et al. Stress hyperglycemia ratio and long-term prognosis in patients with acute coronary syndrome: a multicenter, nationwide study. J Diabetes. 2023;15(7):557–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wang L, Wang C, Lang JC, Xu RD, Cong HL, Zhang JX, Hu YC, Li TT, Liu CW, Yang H, Li WY. The relative and combined ability of stress hyperglycemia ratio and N-terminal pro-B-type natriuretic peptide to predict all-cause mortality in diabetic patients with multivessel coronary artery disease. Cardiovasc Diabetol. 2024;23(1):93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Song Y, Cui K, Yang M, Song C, Yin D, Dong Q, Gao Y, Dou K. High triglyceride-glucose index and stress hyperglycemia ratio as predictors of adverse cardiac events in patients with coronary chronic total occlusion: a large-scale prospective cohort study. Cardiovasc Diabetol. 2023;22(1):180.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Fakhrzadeh H, Sharifi F, Alizadeh M, Arzaghi SM, Tajallizade-Khoob Y, Tootee A, Alatab S, Mirarefin M, Badamchizade Z, Kazemi H. Relationship between insulin resistance and subclinical atherosclerosis in individu-als with and without type 2 diabetes mellitus. J Diabetes Metab Disord. 2015;15:1–7.

    Article  Google Scholar 

  27. Farhan S, Redfors B, Maehara A, McAndrew T, Ben-Yehuda O, De Bruyne B, Mehran R, Vogel B, Giustino G, Serruys PW, Mintz GS, Stone GW. Relationship between insulin resistance, coronary plaque, and clinical outcomes in patients with acute coronary syndromes: an analysis from the PROSPECT study. Cardiovasc Diabetol. 2021;20:1–10.

    Article  Google Scholar 

  28. Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, Martínez-Abundis E, Ramos-Zavala MG, Hernández-González SO, Jacques-Camarena O, Rodríguez-Morán M. The product of triglycerides and glucose, a simple measure of insulin sensitivity comparison with the Euglycemichyperinsulinemic clamp. J Clin Endocrinol Metabol. 2010;95(7):3347–51.

    Article  CAS  Google Scholar 

  29. Sánchez-García A, Rodríguez-Gutiérrez R, Mancillas-Adame L, González-Nava V, Díaz González-Colmenero A, Solis RC, Álvarez-Villalobos NA, González-González JG. Diagnostic accuracy of the triglyceride and glucose index for insulin resistance: a systematic review. Int J Endocrinol. 2020;2020:4678526.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Vasques AC, Novaes FS, de Oliveira MS, Souza JR, Yamanaka A, Pareja JC, Tambascia MA, Saad MJ, Geloneze B. TyG index performs better than HOMA in a Brazilian population: a hyperglycemic clamp validated study. Diabetes Res Clin Pract. 2011;93(3):e98-100.

    Article  CAS  PubMed  Google Scholar 

  31. Zhang Y, Ding X, Hua B, Liu Q, Gao H, Chen H, Zhao XQ, Li W, Li H. High triglyceride-glucose index is associated with adverse cardiovascular outcomes in patients with acute myocardial infarction. Nutr Metab Cardiovasc Dis. 2020;30:2351–62.

    Article  CAS  PubMed  Google Scholar 

  32. Li Y, He S, Wu Z, Li W, Jian W, Cheng Z, Wang C, Shi Y, Liu J. The predictive value of the triglyceride-glucose index for cardiovascular events in patients with coronary chronic total occlusion. Cardiovasc Diabetol. 2022;21:149.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wu Z, Liu L, Wang W, Cui H, Zhang Y, Xu J, Zhang W, Zheng T, Yang J. Triglyceride-glucose index in the prediction of adverse cardiovascular events in patients with premature coronary artery disease: a retrospective cohort study. Cardiovasc Diabetol. 2022;21:142.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yang J, Tang YD, Zheng Y, Li C, Zhou Q, Gao J, Meng X, Zhang K, Wang W, Shao C. The impact of the triglyceride-glucose index on poor prognosis in NonDiabetic patients undergoing percutaneous coronary intervention. Front Endocrinol (Lausanne). 2021;19(12):710240. https://doi.org/10.3389/fendo.2021.710240.

    Article  Google Scholar 

  35. Wang YF, Kong XH, Tao HM, Tao L. The impact of triglyceride-glucose index on the prognosis of post-PCI patients-a meta-analysis. Front Cardiovasc Med. 2024;17(11):1396865. https://doi.org/10.3389/fcvm.2024.1396865.

    Article  Google Scholar 

  36. Zhou Y, Liu L, Huang H, Li N, He J, Yao H, Tang X, Chen X, Zhang S, Shi Q, et al. ’Stress hyperglycemia ratio and in-hospital prognosis in non-surgical patients with heart failure and type 2 diabetes. Cardiovasc Diabetol. 2022;21(1):290.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Mone P, Lombardi A, Salemme L, Cioppa A, Popusoi G, Varzideh F, Pansini A, Jankauskas SS, Forzano I, Avvisato R, et al. Stress hyperglycemia drives the risk of hospitalization for chest pain in patients with ischemia and nonobstructive coronary arteries (INOCA). Diabetes Care. 2023;46(2):450–4.

    Article  CAS  PubMed  Google Scholar 

  38. Xu W, Song Q, Wang X, Zhao Z, Meng X, Xia C, Xie Y, Yang C, Guo Y, Zhang Y, Wang F. Association of stress hyperglycemia ratio and in-hospital mortality in patients with coronary artery disease: insights from a large cohort study. Cardiovasc Diabetol. 2022;21(1):217.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Esposito K, Marfella R, Giugliano D. Stress hyperglycemia, inflammation, and cardiovascular events. Diabetes Care. 2003;26(5):1650–1.

    Article  PubMed  Google Scholar 

  40. Zhong S, Li L, Shen X, Li Q, Xu W, Wang X, Tao Y, Yin H. An update on lipid oxidation and inflammation in cardiovascular diseases. Free Radic Biol Med. 2019;20(144):266–78.

    Article  Google Scholar 

Download references

Funding

This study was funded by Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-055B).

Author information

Authors and Affiliations

  1. Clinical School of Thoracic, Tianjin Medical University, Tianjin, China

    Mingyang Li, Xiaodong Cui, Jiachun Lang, Tianxu Hao & Yuecheng Hu

  2. Department of Cardiology, Tianjin Chest Hospital, 261 Tai’erzhuang Road, Jinnan District, Tianjin, People’s Republic of China

    Mingyang Li, Xiaodong Cui, Jiachun Lang, Tianxu Hao & Yuecheng Hu

  3. Tianjin Union Medical Center, Tianjin Medical University, Tianjin, 300122, China

    Yan Zhang

  4. Tianjin Medical University, Tianjin, China

    Yihang Su

Authors
  1. Mingyang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaodong Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiachun Lang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tianxu Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yihang Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yuecheng Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Mingyang Li, Yuecheng Hu contributed to the study designation. Mingyang Li contributed to manuscript writing, data analysis and editing. Yan Zhang contributed to data analysis. Xiaodong Cui, Jiachun Lang, Yihang Su conducted a critical revision of the manuscript. All authors approved the final manuscript.

Corresponding author

Correspondence to Yuecheng Hu.

Ethics declarations

Ethics approval and consent to participate

The studies involving human participants were reviewed and approved by the Tianjin Chest Hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Consent for publication

The manuscript was approved by all authors for publication.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, M., Cui, X., Zhang, Y. et al. The relative and combined ability of triglyceride-glucose index and stress hyperglycemia ratio to predict major adverse cardio-cerebral events in patients with multivessel coronary artery disease. Diabetol Metab Syndr 16, 234 (2024). https://doi.org/10.1186/s13098-024-01471-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13098-024-01471-0

Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

Contact us