Study on the association between triglyceride-glucose index and clinical characteristics of myocardial infarction in 5,001 patients with coronary artery disease

Introduction

Coronary atherosclerosis (CAS) induced cardiac lesions are referred to as coronary artery disease (CAD) with the former culminating in functional changes, occlusion or lumen stenosis and subsequent ischemia and hypoxia of myocardium. Currently, CAD is considered as the principal cause of mortality globally (1) with the two main subtypes being acute coronary syndrome (ACS) and stable coronary artery disease (SCAD). MI was selected as the key outcome indicator for the following reasons: first, as the most severe acute manifestation of CAD, it has a high incidence and mortality rate (1). Second, previous research by Xing et al. (2) has confirmed that insulin resistance (IR) [assessed by the triglyceride-glucose (TyG) index] associated with acute coronary events. Therefore, MI serves as an important target for risk stratification in patients with CAD.

IR is effectively identified early using the surrogate biomarker like the TyG index (3). A growing body of evidence has established a close association between CAD progression and the TyG index (4). Regarding this, it has been reported that TyG index could be used to predict severity and outcomes of chronic CAS as well as associate with coronary calcification, mortality due to cardiovascular disease (CVD), and urgent revascularisation of coronary (5). As a cost-effective supplementary diagnostic tool that is used to stratify management of prehypertension in lean individuals (3), TyG could additionally serve as an independent risk factor for individuals with type 2 diabetic CAD (6). In recent years, the value of TyG index as a vital indicator in cardiovascular research has attracted growing interest from scientists.

Therefore, this study aimed to further investigate and explore the association between the TyG index and clinical characteristics of MI in patients with CAD. Most previous studies on the TyG index and CAD had focused on specific subgroup populations [e.g., patients with non-alcoholic fatty liver disease (NAFLD), non-diabetic populations] or outcome indicators (e.g., coronary artery calcification, mortality). In contrast, this study sought to explore the association between the TyG index and clinical characteristics of MI in a large, unfiltered cohort of CAD patients, and verify the consistency of this association through subgroup analyses. Although this study only explored the association between the TyG index and clinical characteristics of MI in CAD patients based on a cross-sectional design, it could still provide etiological clues for potential causal relationship between the TyG index and MI. We present this article in accordance with the STROBE reporting checklist (available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-25-42/rc).

Methods

Study population

A cross-sectional study was employed to consecutively recruit 5,001 patients with CAD that were diagnosed at Jiangnan University Medical Center and Jiangsu Provincial Hospital between September 2018 and September 2021 (Figure 1). Diagnosis of the patients encompassed the following criteria: ST-segment elevation MI (STEMI), non-ST-segment elevation MI (NSTEMI), unstable angina pectoris (UAP), and stable angina pectoris (SAP). Inclusion criteria: (I) age >18 years; (II) patients met the diagnostic criteria for CAD, where the diagnosis was determined with reference to the clinical characteristics of MI and in accordance with 2019 Chinese Guidelines for the Diagnosis and Treatment of Acute ST-Segment Elevation Myocardial Infarction (7) and Chinese Guidelines for the Diagnosis and Treatment of Stable Coronary Artery Disease (8). The specific criteria were as follows: (I) acute STEMI: the presence of typical MI symptoms (e.g., persistent crushing pain in the retrosternal area or precordial region lasting >20 minutes, accompanied by sweating, nausea, etc.) is required, along with new ST-segment elevation (≥0.2 mV in leads V2-V3 for males, ≥0.15 mV in leads V2-V3 for females, and ≥0.1 mV in other leads) or newly developed left bundle branch block. Meanwhile, the level of cardiac troponin (cTnI or cTnT) exceeded the 99^th percentile of the upper reference limit with dynamic changes. (II) NSTEMI: The presence of MI symptoms such as chest pain or chest tightness, with no ST-segment elevation on electrocardiogram (ECG) but with ST-segment depression ≥0.05 mV, T-wave inversion ≥0.1 mV, or pathological Q waves (duration ≥0.03 seconds, depth ≥1/4 of the R wave in the same lead). Additionally, the cardiac troponin level exceeded the 99^th percentile of the upper reference limit with dynamic changes. (III) Old myocardial infarction (MI): persistent pathological Q waves on ECG (lasting >3 months) without recent ischemic symptoms; or identification of features of old myocardial necrosis (e.g., local myocardial thinning, abnormal myocardial motion) via examinations such as coronary computed tomography angiography (CCTA) or echocardiography; or a clear history of acute MI with no evidence of recurrence within 3 months. In terms of exclusion criteria, we employed the following: concurrent enrollment in other interventional studies, poor compliance or refusal to participate in follow-up, pregnancy or perinatal status, severe chronic comorbidities (haematological diseases, malignancies), and cognitive impairment or psychiatric disorders.

Figure 1 Inclusion and exclusion flowchart for coronary heart disease patients. BG, blood glucose; NYHA, New York Heart Association; TG, triglyceride.

General data and auxiliary examinations

For each patient at the time of hospital admission, we systematically recorded the clinical baseline data. Age, gender and other demographic variables were taken as personal information. Related health variables, family medical history and body mass index (BMI) were obtained as indicators of health status, while assessments of vital sign, measurements of blood pressure and heart rate were considered as other clinical baseline parameters. In addition, we documented each patient’s detailed past medical history and clinical characteristics at the time of admission.

Group stratification

New York Heart Association (NYHA) Functional Classification: Assessed by attending physicians on the third day after admission (after the patient’s condition stabilised), based on the patient’s symptomatic manifestations during daily activities. To ensure consistency, all assessment results were further reviewed and confirmed by senior cardiologists. Killip classification: for patients with acute myocardial infarction (AMI), this classification was comprehensively evaluated by attending physicians on the first day after admission (within 12 hours of AMI onset), combining the patient’s clinical signs and chest X-ray findings. Additionally, in this study, the TyG index was analysed as a stratifying variable and categorised into four groups based on its quartile levels. This grouping was conducted to investigate the association between the TyG index and diagnosis of MI in patients with CAD. The selection of quartile cutoff values for the TyG index in this study was based on the following reasons: First, there is no clear clinical cutoff value for the TyG index in clinical practice. Second, according to other previous high-quality studies, the quartile grouping method demonstrates a certain degree of reliability and can reflect the dose-response relationship between the two variables [Huo et al. (9), Rowe et al. (10)]. TyG index quartile classification: based on TyG index values, we stratified the patients into four distinct quartiles, namely Q1 (n=1,254): TyG index ≤8.31, Q2 (n=1,243): TyG index ranging from >8.31 to ≤8.69, Q3 (n=1,256): TyG index between >8.69 and ≤9.15, Q4 groups (n=1,248): TyG index >9.15. Clinical characteristic-based grouping: angina pectoris (AP) group: in this group, we combined SAP and UAP presentations. MI group: consolidated STEMI and NSTEMI cases. Functional severity stratification, NYHA classification: low severity: classes I–II, high severity: classes III–IV. Killip classification: low risk: classes I–II, and high risk: classes III–IV.

Laboratory examinations

Blood samples were collected from the patients after hospital admission. For patients diagnosed with AMI at the time of presentation: fasting blood samples were collected on the first day of admission (within 24 hours of AMI onset and before the initiation of AMI-specific treatment). For patients with a history of previous MI or SAP: fasting blood samples were collected during routine admission or follow-up visits (with no AMI occurring at the time of sampling). The laboratory testing methods for fasting blood glucose (FBG) and triglyceride (TG) were fully standardised between the two hospitals: (I) testing methods and reagents: FBG was measured using the hexokinase method, while TG was measured using the enzymatic method. Both tests utilised the Roche Cobas c702 reagent kit, ensuring consistency in testing principles and reagents. (II) Quality control procedures: before daily testing, the instruments were calibrated with Roche standard materials. Meanwhile, low-, medium-, and high-concentration internal quality control samples were tested daily to ensure the accuracy and stability of results. Additionally, both centres participated in the external quality assessment programme organised by the National Centre for Clinical Laboratories of China every quarter, with a 100% pass rate, verifying the consistency of results across laboratories. (III) Cross-validation results: fifty pairs of matched samples were tested simultaneously at both centres. The correlation coefficient was 0.98 for FBG and 0.97 for TG, thereby confirming a high correlation between the test results of the two centres and good consistency in the testing methods. Other relevant biochemical test indicators were measured by the hospital laboratory using standard experimental methods. Calculation of TyG index was carried out using the following equation:

TyGindex=ln⁡[TG(mg⋅dL−1)×FBG(mg⋅dL−1)2][1]

Statistical analysis

In this study, the mean imputation method was used to perform multiple imputation for other included covariates with missing values. Mean ± standard deviation (x±s) was used to express measurement data that conformed to a normal distribution, while intergroup comparisons were performed using one-way analysis of variance (ANOVA). Median and interquartile range were utilised to present measurement data with non-normal distribution, while intergroup comparisons were carried out using the rank-sum test. The Chi-squared test or Fisher’s exact test was used for intergroup comparisons, while frequencies and percentages (n, %) were used to express categorical data. The relationship between the TyG index and clinical characteristics of patients with CAD was analysed using multivariate logistic regression analysis. SPSS 26.0 statistical software was applied to analyse all data, while level of statistical significance was set at a P<0.05. Herein, the selection of covariates in the logistic regression model was first based on the known CAD-related confounding variables (e.g., age, sex, hypertension) from the 2022 Report on Cardiovascular Health and Diseases in China (1) and the study by Xing et al. (2). Secondly, several important covariates were selected because they have significant effects on the occurrence and progression of CAD. In the logistic regression model, all selected variables were entered simultaneously to adjust for potential important covariates, aiming to explore the independent association between the TyG index and diagnosis of MI in patients with CAD. To explore the consistency of the association between the TyG index and diagnosis of MI across different populations in patients with CAD, we further conducted subgroup analyses. The stratification variables included age (<65 or ≥65 years), gender (male or female), smoking history (present or absent), BMI (<24 or ≥24 kg/m²), systolic blood pressure (<140 or ≥140 mmHg), history of hypertension (present or absent), previous PCI history (present or absent), and previous stroke history (present or absent).

Ethics

The study conformed to the provisions of the Declaration of Helsinki and its subsequent amendments. The current study was approved by the Ethics Committee of the Jiangnan University Medical Center (No. 2022Y-174). Jiangsu Provincial Hospital were also informed and agreed on the study. Individual consent for this retrospective analysis was waived.

Results

General characteristics of patients with CAD

A total of 5,001 patients with CAD were included in this study. Patients with higher TyG index levels exhibited higher systolic blood pressure (SBP), diastolic blood pressure (DBP), and BMI. The proportion of patients with a history of hypertension increased with an elevation of TyG index levels (P=0.001). Additionally, we found significant differences amongst the four groups regarding heart rate, age, previous history of coronary artery bypass grafting (CABG) surgery, atrial fibrillation (AF) and cerebral infarction (see Table 1 for P values). Details are shown in Table 1.

Analysis of the relationship of clinical characteristics of MI in CAD patients with TyG index

Patients were grouped according to the quartiles of TyG index levels, with the lowest quartile group serving as the reference. The association between TyG index and clinical characteristics of patients with CAD was analysed. Results of multivariate logistic regression analysis, after adjusting for potential confounders such as age, sex, and other important covariates, showed that TyG index level in CAD patients independently associated with diagnosis of MI. With an increase in TyG level, the strength of the association with diagnosis of MI in CAD patients gradually increased [Q2: odds ratio (OR) =1.31, 95% confidence interval (CI): 1.07–1.60; Q3: OR =1.39, 95% CI: 1.13–1.71; Q4: OR =1.72, 95% CI: 1.38–2.15; P<0.001]. However, the association between TyG index and patients’ NYHA functional classification was not statistically significant (Table 2).

It was found through the analysis that an increase in TyG index level significantly associated with an elevated risk of MI diagnosis in CAD patients. Therefore, the relationship between TyG index and Killip classification was further explored in 1269 MI patients. The results showed that the association between TyG index and Killip classification was not statistically significant (Q2: OR =0.86, 95% CI: 0.41–1.83; Q3: OR =0.97, 95% CI: 0.44–2.13; Q4: OR =0.80, 95% CI: 0.33–1.92; P<0.001) (Table 3).

Subgroup analysis of ORs (95% CI) for the relationship of clinical characteristics in CAD patients with TyG index

To further clarify the relationship between the TyG index and clinical characteristics of MI in patients with CAD, subgroup analyses were conducted by grouping variables such as age, sex, smoking history, BMI, SBP, history of hypertension, history of previous PCI, and history of previous stroke (including a history of previous cerebral infarction and a history of previous cerebral haemorrhage). The results showed that except for the subgroup stratified by history of previous stroke, the differences between the TyG index and clinical characteristics of MI in patients with CAD were statistically significant in all other subgroups (see Tables 4,5 for P values), Tables 4,5 present detailed results. (Note: all ORs presented in Tables 4,5 represent the overall effect trend of the TyG index when categorised as a quartile variable, while the statistical significance of this overall effect trend is reflected by the P value.)

Discussion

Summary of results

Currently, the number of patients with CAD in China is approximately 20 million. This issue not only imposes a heavy burden on the physical and mental health as well as economic well-being of patients and their families but also leads to a substantial disease burden on society. The purpose of this study was to explore the relationship between the TyG index and clinical characteristics of MI in patients with CAD, so as to provide potential scientific value and clues for the clinical prevention, diagnosis and treatment of patients with CAD.

Our study results showed that with an increase in TyG index, the strength of the association between TyG index and diagnosis of MI in patients with CAD gradually increased compared with angina patients (Q2: OR =1.31, 95% CI: 1.07–1.60; Q3: OR =1.39, 95% CI: 1.13–1.71; Q4: OR =1.72, 95% CI: 1.38–2.15; P<0.001). However, no statistically significant differences were observed between TyG index and NYHA functional classification or Killip classification.

Additionally, in subgroup analyses stratified by age, sex, smoking history, BMI, SBP, history of hypertension, history of PCI, and history of previous stroke (including both cerebral infarction and cerebral haemorrhage), our results showed that, except for the subgroup with a history of previous stroke, the associations between the TyG index and clinical characteristics of MI in CAD patients were statistically significant across all other subgroups.

Comparison with other studies and discussion

Accumulating evidence has indicated that IR was closely linked to progression of CAD (11). In 2008, Simental-Mendía et al. first proposed the use of fasting TG and FBG as surrogate markers to identify IR (12), which laid the foundation to utilise TyG index in clinical application. Relevant studies have further validated the association between TyG index and CAD. Zhao et al. (4) conducted research work that involved 424 chest pain and NAFLD patients. The authors found that the main risk factor for CAD in NAFLD patients was TyG index. A positive correlation was observed between the TyG index and CAD risk in this population, suggesting that the severity of CAS may be estimated using TyG index. Additionally, Xing et al. (2) included 7,835 non-diabetic participants in their study and concluded that attenuation of IR in non-diabetic individuals might be a suitable target for CVD prevention strategies, further supporting the role of IR (assessed by the TyG index) in CVD pathogenesis. Numerous studies have also highlighted the association between TyG index and development and prognosis of CVD. Kouvari et al. (13) performed a prospective epidemiological study that involved 1,528 women and 1,514 men (>18 years old), wherein they discovered the potential of TyG index to predict long-term CVD onset. However, we did not observe prognostic effect of TyG index on the recurrence of CVD, which poses challenges for its clinical application in secondary prevention. In a retrospective study of 715 intermediate-risk chronic coronary syndrome (CCS) patients who visited outpatient clinics between June 2020 and August 2022, Erdogan et al. (14) suggested that the TyG index may better serve as indicator to predict poor CVD outcomes in CCS patients. In the Chinese population, Pang et al. (15) analysed data from 3,614 hypertensive patients in a health and nutrition survey and found that TyG index closely associated with all-cause mortality in middle-aged and elderly hypertensive patients. Furthermore, Zhao et al. (16) observed that TyG index positively correlated with incidence of chest pain in the U.S. population. Taken together, these findings indicate a close association of TyG index with risk factors for cardio-cerebrovascular diseases (CCVDs).

Against this background, we hypothesised that TyG index may closely relate to occurrence, development, and severity of CCVDs. Therefore, this study focused on patients with CAD, and specifically explored the potential association between the TyG index and diagnosis of MI in this population. It aims to provide potential scientific evidence and practical clues for the clinical prevention, early identification, and optimisation of diagnosis and treatment of MI in CAD patients.

Innovations and limitations

First, the findings of this study are based on a cross-sectional study design. Although this design provides preliminary clues for the association between the TyG index and MI, it cannot confirm a causal relationship between them. Further verification through large-sample prospective cohort studies is required in the future. Second, the samples of this study were only collected from two hospitals, which limits the representativeness of the samples. In the future, multi-centre clinical studies should be conducted to more accurately determine the correlation between the TyG index and clinical characteristics of MI in patients with CAD. Third, although this study adjusted for several potential important covariates when analysing the association between the TyG index and clinical characteristics of MI in CAD patients, some relevant covariates were not included. In-depth exploration of the causal relationship between the two will be improved in future studies.

However, this study also has the following strengths: First, the large sample size ensured a certain degree of representativeness of the research results. Second, the study subjects included not only MI patients but also AP patients. Through comparative analysis, the core conclusion that “compared with AP patients, the TyG index associated with MI” was confirmed. Third, potential important covariates were adjusted in the association analysis, which reflects the independent association between the two to a certain extent. Fourth, stratified analysis was conducted based on different potential important covariates, further verifying the stability of the study results.

Conclusions

In conclusion, this study confirmed that there is an association between the TyG index and clinical characteristics of MI in patients with CAD. Although this study could not directly confirm the causal relationship between the two, it still provided key clues for further exploration of this causal association in subsequent research. Based on the current results, the TyG index is expected to be a potential biomarker for assessing the risk of MI in CAD patients, while its clinical application value still needs to be further verified through prospective cohort studies.

Acknowledgments

We express our sincere gratitude to all patients and researchers who contributed to this study.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-25-42/rc

Data Sharing Statement: Available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-25-42/dss

Peer Review File: Available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-25-42/prf

Funding: This work was supported by the Scientific research project of Wuxi Municipal Health Commission (No. M202207) and Wuxi Double Hundred Top Talents in Healthcare (Nos. BJ2023029, and HB2023026).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-25-42/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study conformed to the provisions of the Declaration of Helsinki and its subsequent amendments. The current study was approved by the Ethics Committee of the Jiangnan University Medical Center (No. 2022Y-174). Jiangsu Provincial Hospital were also informed and agreed on the study. Individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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