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Comparison of TyG and modified TyG indices in predicting coronary slow flow phenomenon

BMC Cardiovascular Disorders volume 25, Article number: 340 (2025) Cite this article

Abstract

Background

The coronary slow flow phenomenon (CSFP) represents a common condition in patients with ischemia and non-obstructive coronary artery disease (INOCA). The triglyceride-glucose index (TyG) and relative modified indices have been established to be associated with CSFP. However, comparison of the clinical value of TyG and its modified indices in predicting CSFP has not been evaluated.

Materials and methods

INOCA patients were retrospectively enrolled. According to the corrected TIMI counts, the patients were divided into the CSFP group and the non-CSFP group. A total of 4,627 patients were enrolled in our study. Among them, 69 patients were divided into the CSFP group, while 586 patients were divided into the non-CSFP group. Demographic, clinical risk factors, and laboratory results, including TyG and its modified indices, were compared between the two groups. The prognostic value of TyG and its modified indices in CSFP was compared using the area under the curve (AUC).

Results

Most of the demographic and clinical risk factors between CSFP patients and non-CSFP patients were comparable. For patients with CSFP, the patients were more likely to have chronic kidney disease (CKD) (39.13% vs. 20.31%, p < 0.001) and less likely to have anti-diabetic therapy (14.49% vs. 27.13%, p = 0.023). The patients with CSFP also had higher body weight index (BMI) (p < 0.001), higher levels of uric acid (UA), triglyceride (TG) (p = 0.017), total cholesterol (TC) (p = 0.016) and low-density lipoprotein cholesterol (LDL-C) (p = 0.006), homocysteine (p < 0.001) and uric acid (p < 0.002). Both TyG and its modified indices, including TyG-WC, TyG-BMI were demonstrated to be independently associated with CSFP in multi-variable logistic analysis after adjusting other co-variables.Further receiver operating characteristic (ROC) curve demonstrated that TyG-WC showed the best performance in predicting CSFP compared with other indices. Subgroup analysis revealed that the predictive value of TyG-WC in CSFP was consistent in different subgroups except that the predictive value was better in male patients compared with female patients,

Conclusions

Our investigation reveals that TyG, TyG-WC and TyG-BMI were independent risk factors for CSFP. TyG-WC showed a better predictive performance than other indices in predicting CSFP.

Graphical Abstract

Peer Review reports

Introduction

Chest pain is a frequent reason for emergency visits, with cardiac causes including acute coronary syndrome (ACS) and non-cardiac etiologies. The diagnose of chest pain usually requires complex and sometimes invasive diagnostic methods for a conclusive diagnosis [1,2,3,4]. Coronary angiography (CAG) is pivotal for diagnosing ACS, but some patients exhibit chest pain without obstructive coronary arteries, classified as ischemia with no obstructive coronary arteries (INOCA) [5, 6]. Among INOCA patients, CSFP is a potential etiology, though its pathogenesis remains unclear [7, 8].

The detailed mechanism of CSFP are still unclear, however, inflammation and insulin resistance are closely related to the development of CSFP [7, 9–10]. The triglyceride-glucose ratio (TyG), initially proposed as a surrogate marker of insulin resistance, is now considered a novel inflammation parameter, and its clinical value has been extensively investigated in various cardiovascular disorders [11,12,13]. Recently, its clinical value in predicting CSFP has also been noted in some studies [14,15,16,17]. Moreover, the modified TyG indices, which combine TyG with other parameters such as body mass index, waist circumference, and waist-to-height ratio, have exhibited favorable predictive value in diseases including CSFP [18,19,20,21].

The clinical value of TyG and its indices in CSFP have been discussed before, however, which parameter has the best predictive value is still unclear. Our study, so far as we know, is the first study aiming to compare the clinical value of these indices in CSFP.

Methods

Study population

This retrospective study was performed at Beijing Tiantan Hospital, Capital Medical University. The detailed study information has been discussed in our previous study [22]. In conclusion, patients with chest pain and underwent CAG in our department from January 1st, 2022 to August 31st, 2023 were retrospectively enrolled. Patients with normal coronary arteries which confirmed by CAG were diagnosed with INOCA. Patients who were younger than 18 years old, patients with stenotic coronary arteries, patients with symptomatic heart failure, patients with severe hepatic or renal diseases, patients with aortic dissection or pulmonary embolism, patients with cardiomyopathy were excluded.

Data collection

The baseline demographic data and laboratory results were analyzed. Clinical information including demographic information (age, gender smoking history) and previous medical history (hypertension, diabetes mellitus, dyslipidemia, coronary artery disease, cerebrovascular disease) were collected. The prior medication use as well as body weight, height, and waist circumference, were also collected. The calculation of the TyG index and its modified indices was illustrated in previous studies [14,15,16,17,18,19,20,21].

CSFP was diagnosed on the CAG results evaluated by professional cardiologists. The corrected TIMI frame count (CTFC) was used to evaluate the coronary blood flow and patients with CTFC more than 27 in at least one coronary artery were considered as CSFP [23].

Statistical analysis

Variables were shown as numbers and frequencies (percentages) or mean ± standard deviation or median (first quartile, third quartile), as appropriate. Categorical variables were compared with the chi-square test, continuous variables were compared with independent sample t-test or Mann-Whitney test. Multi-variable logistic analysis was employed to assess the associations between TyG and its modified indices and CSFP. Three models were used to adjust the confounding co-variables. In Model 1, no other co-variables were adjusted. In Model 2, gender, smoking, age, body mass index (BMI), chronic kidney disease, and anti-diabetic treatment were adjusted. In Model 3, gender, age, smoking, BMI, aspartate aminotransferase (AST), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), uric acid (UA), B-type natriuretic peptide (BNP), estimated glomerular filtration rate (eGFR), homocysteine (HCY), chronic kidney disease, and antidiabetic treatment were adjusted. To explore the dose-response relationship between TyG and its modified indices and the development of CSFP, logistic regression models incorporating restricted cubic splines (RCSs) for smooth curve fitting were also performed. The receiver operating characteristic (ROC) curve was applied to evaluate the predictive value of different indices. The comparison of the ROCs was used by net reclassification index (NRI) and integrated discrimination improvement (IDI). Data processing and analysis were performed using R version 4.3.0 (2023-06-21), along with Storm Statistical Platform (www.medsta.cn/software).

Results

A total of 4,627 patients underwent CAG, of whom 655 were identified as INOCA. These patients were subsequently divided into two groups: the CSFP group (n = 69) and the non-CSFP group (n = 586). The study flowchart is presented in Fig. 1.

Fig. 1
figure 1

Study flow chart

Full size image

The demographic data, prior medical history, and medication use history were compared between the CSFP group and the non-CSFP group, with the results summarized in Table 1. Overall, most parameters were comparable between the two cohorts. However, significant differences were observed in certain aspects. Patients in the CSFP group exhibited a significantly higher body mass index (BMI) compared to those in the non-CSFP group [26.56(25.35,28.72) vs. 25.56 (23.11,27.68),kg/m2, p < 0.001]. Additionally, the prevalence of CKD was significantly higher in the CSFP group than in the non-CSFP group (39.13% vs. 20.31%, p < 0.001).

Table 1 The comparison results of demographic data, prior medical history, and medication use history between the CSFP group and the non-CSFP group
Full size table

The baseline laboratory parameters were compared between the CSFP group and the non-CSFP group, with the results detailed in Table 2. In summary, patients in the CSFP group exhibited significantly higher levels of AST [21.10(17.30,25.80) vs. 19.00(16.30,24.00) U/L, p = 0.026], UA [384.90(308.90,443.70) vs. 337.20(283.18,402.45) mmol/L, p = 0.002], HCY[14.13(11.21,20.86) vs. 12.28 (9.98,15.75) mmol/L, p < 0.001], and BNP [241.70 (166.90,329.60) vs. 42.45 (20.18,97.87) pg/ml, p < 0.001]. Additionally, the levels of TC, TG, and LDL-C were also higher in the CSFP group compared to the non-CSFP group.

Table 2 The comparison results of baseline laboratory parameters between the CSFP group and the non-CSFP group
Full size table

Furthermore, the comparison of TyG and its modified indices between the CSFP and non-CSFP patients is also presented in Table 2.Compared with the non-CSFP group, the CSFP group demonstrated significantly higher levels of TyG (9.03 ± 0.61 vs. 8.73 ± 0.63, p < 0.001), TyG-WC [918.96 (832.54,997.66) vs. 834.09 (770.57,903.21), p < 0.001], TyG-BMI (241.56 ± 38.69 vs.222.17 ± 38.36, p < 0.001),and TyG-WHR [5.5(5.1,5.8) vs.5.0(4.6,5.4), p < 0.001].

To assess the potential nonlinear correlation between the indices and CSFP, we carried out Restricted Cubic Splines (RCS) fitting within the Logistic regression models. The findings suggested that some relationships are significantly non-linear while others may be closer to a linear relationship without statistic significance, as depicted in Fig. 2.

Fig. 2
figure 2

Restricted cubic splines for TyG and its modified indices with CSFP

Full size image

Subsequently, we proceeded with a multi-variable logistic regression analysis to appraise the association between these parameters and CSFP. The detailed results of the logistic regression analysis are presented in Table 3. Before we perform the multi-variable logistic analysis, the potential collinearity of the enrolled variables were tested and the results showed that there were moderate collinearity relationship between TG and TyG, TyG-WC, TyG-BMI and TyG-WHR. In conclusion, after adjusting for other confounding variables, TyG, TyG-WC, TyG-BMI except TyG-WHR emerged as independent risk factors for CSFP.

Table 3 Logistic regression analysis of TyG and its modified indices with CSFP
Full size table

The predictive value of TyG and its modified parameters in CSFP were compared using the ROC curve. In summary, both TyG and its modified parameters have good predictive value in CSFP. The area under the curve (AUC) of TyG, TyG-WC, TyG-BMI, and TyG-WHR in predicting CSFP are 0.648 (0.580–0.717), 0.714 (0.647–0.781), 0.655 (0.586–0.723), 0.705 (0.644–0.767), respectively (Fig. 3). We also compared the predictive value between TyG-WC and other parameters in CSFP and TyG-WC has better performance when compared with TyG and other modified parameters using net reclassification improvement and integrated discrimination improvement (Table 4).

Fig. 3
figure 3

The ROC curve of TyG and its modified indices in predicting CSFP

Full size image
Table 4 The comparison results of net reclassification improvement and integrated discrimination improvement between TyG-WC and other indices in CSFP
Full size table

We further evaluated the predictive value of TyG-WC in different subgroups. The detailed results of TyG-WC in CSFP among different subgroups were shown in Table 5. The results demonstrated that TyG-WC was significantly associated with CSFP in patients with different age, BMI and smoking history. However, for male patients, the predictive value of TyG-WC in CSFP was not statistically significant.

Table 5 Results of subgroup analysis of TyG-WC in CSFP
Full size table

Discussion

In this retrospective study, we investigated the clinical value of TyG and its modified indices in predicting CSFP.As one of the largest studies in this area, we identified two key findings. First, we demonstrated that TyG, TyG-WC, and TyG-BMI are all independent risk factors for CSFP in patients with INOCA. Second, our analysis revealed that TyG-WC exhibited superior predictive performance in identifying CSFP compared to other TyG-related indices.

TyG has recently emerged as a novel biomarker for a wide range of diseases [24]. It has been extensively reported that TyG is associated with both cardiovascular and non-cardiovascular conditions [25,26,27,28]. However, the correlation between TyG and CSFP has been underexplored. In a retrospective study by Kaplangoray et al.,118 CSFP patients and 105 non-CSFP patients were analyzed, and multivariate analysis revealed that TyG was an independent predictor of CSFP, with an AUC of 0.868 [14]. Similarly, studies by Bilen et al.and Yuksel et al.also demonstrated that the TyG index is an independent predictor of CSFP [15, 16]. Another study involving 1,100 participants further confirmed TyG as an independent risk factor for CSFP [17].These findings are consistent with our results.

The clinical value of TyG-modified indices has also been evaluated in numerous studies. For instance, Cheng Y and colleagues examined the relationship between TyG-BMI and cardiovascular outcomes in patients undergoing percutaneous coronary intervention. After adjusting for other variables, TyG-BMI was found to be correlated with the incidence of adverse clinical events [29]. In a retrospective study by Li ZP et al., 1625 INOCA patients were analyzed, and the TyG-BMI index was identified as an independent predictor of CSFP [19]. Xue Y et al.discovered that TyG-WC was positively associated with liver stiffness and exhibited the strongest diagnostic capacity among related indices [26]. Additionally, Xuan W found that TyG-WHtR was independently associated with the risk of developing diabetes [30].

The comparative analysis of TyG and its modified indices has also been explored in previous studies. Miao H and colleagues compared the capabilities of TyG and its modified parameters in identifying patients with hypertension and high cardiovascular risk. After adjusting for other confounding factors, both TyG and the modified parameters demonstrated favorable predictive values. Among these, TyG-WC exhibited the highest diagnostic effectiveness for hypertension [31]. However, in another study by Yang C et al., TyG and its modified indices were evaluated, and the results indicated that TyG-WHR showed the best performance in predicting newly onset hypertension in the general population [32]. Similarly, Li X et al.assessed the predictive value of TyG and its modified indices in newly onset diabetes, and the findings revealed that TyG-WHR outperformed other parameters in this context [33].

The pathogenesis of CSFP is multifaceted and complex. Endothelial dysfunction, inflammation, oxidative stress, and insulin resistance have all been implicated as key factors associated with CSFP [7, 8, 34]. Previous studies have demonstrated that endothelium-dependent flow-mediated dilatation function is compromised in patients with CSFP. Additionally, cellular factors that influence endothelial vasoconstriction or vasodilation are often imbalanced, as shown in various studies [35,36,37].

Inflammation is another potential mechanism underlying CSFP. Elevated levels of high-sensitivity C-reactive protein and other inflammatory biomarkers have been observed in patients with CSFP in prior research [38, 39]. Oxidative stress is also considered a possible mechanism, as evidenced by higher levels of malondialdehyde and superoxide dismutase in CSFP patients, indicating that oxidative stress may impair endothelial function [40, 41].

These mechanisms are likely interconnected and may collectively contribute to the development of CSFP. Our findings are consistent with these previously established mechanisms, suggesting that the associations we observed may be mediated through these pathways.

In our study, the TyG-WC exhibited an AUC value of 0.714,indicating only a modest discrimination ability. However, it is still worthy since some well-known risk scores, including the Framingham risk score, also demonstrate modest discrimination ability [42, 43]. Given its simplicity and non-invasive nature, TyG-WC may still prove useful as a screening tool for identifying CSFP patients prior to undergoing CAG.

The underlying mechanism for the superiority of TyG-WC over other TyG indices in predicting CSFP remains unclear. TyG is a well-accepted biomarker for insulin resistance, while waist circumference (WC) is a well-established indicator of visceral adiposity. Both TyG and WC are potentially associated with atherosclerosis and inflammation, which are key mechanisms underlying CSFP [7, 34]. The combination of TyG and WC may therefore provide a more prominent association with CSFP compared to other TyG indices.

In conclusion, to the best of our knowledge, this is the first study to compare the predictive values of TyG and its modified indices in predicting CSFP. Our findings indicate that TyG-WC exhibits relatively superior discriminative ability in predicting CSFP compared to other indices.

Our study has several limitations. First, as a single-center retrospective study, our preliminary findings require further validation through multi-center studies.An external validation cohort with a larger and more diverse group of participants is necessary to confirm our results. Second, the prognostic value of TyG and its modified indices regarding the long-term clinical outcomes of CSFP patients remains to be explored in future studies. Third, our study only included patients with chest pain, which could introduce potential selection bias. Finally, the measurement of waist circumference and BMI relied on the accuracy of the measurement process and may have been subject to observer variability.These factors could also represent potential limitations in our study.

Data availability

No datasets were generated or analysed during the current study.

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Authors and Affiliations

  1. Department of Cardiology and Macrovascular Disease, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

    Jing Nan, Shuai Meng, Ruofei Jia, Wei Chen, Xingsheng Yang, Hongyu Hu, Lijun Han & Zening Jin

  2. No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China

    Lijun Han

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Contributions

J. N collected the data, and wrote the paper S. M, RF. J, HY. H, XS. Y collected the data and analyzed the TIMI flow counts. ZN. J revised the data and wrote the paper. LJ. H collected the data and supervised the writing. All of the authors reviewed the manuscript.

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Correspondence to Lijun Han.

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Ethics approval and consent to participate

This research was carried out in line with the Helsinki Declaration. Written informed consents were retrieved from patients and/or their authorized relatives, and the ethical clearance was obtained from Beijing Tiantan Hospital, affiliated with Capital Medical University (KY-23-037). Due to the nature of our study, we did not registered this study so the clinical trial number was not applicable.

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All of the authors agree to publish this study.

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The authors declare no competing interests.

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Nan, J., Meng, S., Jia, R. et al. Comparison of TyG and modified TyG indices in predicting coronary slow flow phenomenon. BMC Cardiovasc Disord 25, 340 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12872-025-04794-6

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12872-025-04794-6

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BMC Cardiovascular Disorders

ISSN: 1471-2261

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