Association of the triglyceride-glucose index with severity of coronary stenosis and in-hospital mortality in patients with acute ST elevation myocardial infarction after percutaneous coronary intervention: a multicentre retrospective analysis cohort study

Abstract

Objectives

To explore the impact of the triglyceride-glucose (TyG) index on the severity of coronary stenosis and the risk of in-hospital mortality in patients with acute ST segment elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI).

Design

A multicentre retrospective cohort study.

Setting

Patients with STEMI undergoing PCI from three centres in China from 2015 to 2019.

Participants

A total of 1491 individuals presenting with STEMI were enrolled.

Primary outcome measure

The degree of coronary stenosis was quantified by the Gensini score (GS). The association between the TyG index and the severity of coronary stenosis was explored by using a logistic regression analysis. Cox proportional hazards regression analyses were used to investigate the associations between the variables and in-hospital mortality.

Results

We found a significant correlation between the TyG index and the degree of coronary stenosis in the present study. The TyG index was an independent risk factor for the severity of coronary stenosis (OR 2.003, p<0.001). Using the lowest tertile of the TyG (T1) group as a reference, the adjusted ORs for the T2 group and the T3 group and a high GS were 1.732 (p<0.001), 1.968 (p<0.001), respectively, and all p for trend <0.001. For predicting a high GS, the TyG index’s area under the curve was 0.668 (95% CI 0.635 to 0.700, p<0.001). Additionally, the TyG index was further demonstrated to be an independent predictor of in-hospital mortality in patients with STEMI (HR 1.525, p<0.001).

Conclusions

The TyG index was associated with the severity of coronary stenosis and all-cause in-hospital mortality in patients with STEMI, which may help physicians precisely risk-stratify patients and implement individualised treatment.

Correspondence to Xiaohu Chen; [email protected]

STRENGTHS AND LIMITATIONS OF THIS STUDY

• This study was a multicentre, retrospective, observational cohort study.

• The present study included a relatively large number of patients with ST segment elevation myocardial infarction and an extensive range of available laboratory indexes.

• Limitations include that this study only enrolled the Chinese population, which may not reflect the whole cohort, and all measurements and laboratory parameters were assessed only once during follow-up.

Introduction

Even though great progress has been made in pharmacotherapy and myocardial reperfusion, the global burden of acute myocardial infarction (AMI) continues to pose significant challenges in terms of morbidity and mortality rates.¹ Consequently, effective early risk stratification is crucial for managing and preventing AMI.^{2 3} Insulin resistance (IR), a marker of systemic inflammation and metabolic disorders, plays a critical role in the pathophysiology and progression of cardiovascular disease (CVD).⁴ Recently, the triglyceride-glucose (TyG) index, combined with fasting blood glucose (FBG) and triglyceride, has been shown to be a reliable, non-invasive, simple and effective surrogate indicator of IR.^{5 6} Mounting evidence demonstrates that the TyG index is associated with the incidence rates of hypertension, coronary artery disease (CAD), coronary artery calcification (CAC) and myocardial infarction.^7–9 Recently, several studies have indicated that the TyG index may be an effective indicator of the complexity and severity of CAD.^{10 11} The relationship between the TyG index and the degree of coronary stenosis in individuals with acute ST segment elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI) remains, however, poorly known. Furthermore, the studies regarding the influences of the TyG index on the poor prognosis in patients suffering from STEMI are extremely limited. Therefore, one of our aims was to assess the relationship between the TyG index and the severity of coronary stenosis using the Gensini score (GS) in patients with STEMI undergoing PCI. Moreover, we further explored and evaluated the impact of the TyG index on the in-hospital all-cause mortality risk of patients with STEMI after PCI for the purpose of providing ideas for improving STEMI risk stratification.

Materials and methods

Patients and study design

This study was a multicentre, retrospective, observational cohort study conducted at three hospitals in Nanjing, China (Affiliated Hospital of Nanjing University of Chinese Medicine, the First Affiliated Hospital of Nanjing Medical University and the Affiliated Brain Hospital of Nanjing Medical University). A total of 1654 consecutive patients with STEMI admitted from January 2015 to December 2019 were included in the study. This study strictly followed the Declaration of Helsinki and was approved by our local institution’s ethical committee. Informed consent was given by each subject. The STEMI was defined based on the Guidelines of the European Society of Cardiology (ESC).¹² Exclusion standards included chest pain for more than 24 hours, severe liver or kidney disease (serum creatinine (Scr) >1.4 mg/dL or liver function parameters >3×upper normal value), evidence of active viral or bacterial infection, history of advanced malignancy, severe trauma over the past 3 months and absence of clinical/laboratory data and coronary angiography (online supplemental figure 1). Finally, 1491 patients were enrolled in this study.

Data collection and definitions

All sociodemographic characteristics, clinical case histories, laboratory investigations and medical information of patients were retrieved from the electronic medical records. The blood samples from participants for laboratory tests were obtained after more than 8 hours of fasting. Laboratory data, including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), FBG, Scr and albumin, were performed using an automatic biochemistry analyser (AU2700, Japan). The assessment of the risk within this study population was quantified using the Thrombolysis in Myocardial Infarction (TIMI) risk score for STEMI,¹³ which was calculated for all patients on admission. The calculation of the TyG value was defined as ln(fasting triglyceride mg/dL×fasting glucose mg/dL/2).

Coronary angiography and GS

Coronary angiography was performed by the standard Judkins method for all patients. Two separate cardiologists independently assessed the GS to determine the severity of coronary stenosis.¹⁴ Patients were then separated into groups with mild (GS ≤60) and severe (GS >60) coronary artery stenosis.

Patient and public involvement

Patients or the public were not involved in the design, or conduct, or reporting or dissemination plans of our research.

Statistical analysis

Quantitative data with normally distribution were described as the mean with SD, while quantitative data with skewed distribution were described as the median with IQR. The differences among the groups were assessed by one-way analysis or Kruskal-Wallis test. Categorical data were described as percentages, and χ² analysis was used to compare between the groups. The correlation between the GS and TyG index was determined by Spearman’s correlation analysis. The logistic regression and Cox regression were used to investigate the factors influencing the severity of coronary stenosis and in-hospital mortality. The receiver operating characteristic (ROC) curve was performed to explore the independent importance of the TyG index for the severity of coronary stenosis. Restricted cubic splines (RCS) were used to assess the non-linear correlation between the TyG index and the severity of coronary stenosis. To validate the correlation and explore the possibility of a non-linear correlation between the TyG index and the severity of coronary artery stenosis and in-hospital mortality, the TyG index was converted into a categorical variable by tertiles, and the p for trend was calculated. The TyG index was input as continuous data or stratified by tertiles for the multivariable analysis in two independent models. Furthermore, the Kaplan-Meier method was used to construct the cumulative survival curves for in-hospital mortality with different tertiles of the TyG index. All statistical analyses were performed by SPSS V.26.0 software (IBM, Chicago, Illinois, USA) and R V.4.0.2 software (R Foundation for Statistical Computing, Vienna, Austria). All analyses were two-tailed, and p<0.05 was considered statistically significant.

Results

Baseline characteristics

Among the 1491 patients, 79.7% were male, and the average age was (64.22±12.32) years. According to the TyG index tertiles (T1, TyG≤8.54; T2, 8.54<TyG≤9.08; T3, TyG>9.08), table 1 presented the study cohort’s baseline characteristics. Patients with higher TyG index tertiles tended to be older and are male than those with lower TyG index tertiles (p<0.001，p=0.017, respectively). Participants with a higher TyG index were more likely to have higher prevalences of hypertension, diabetes mellitus and alcohol use compared with those in the T1 group (both p<0.05), while stroke history and smoking status did not differ significantly across TyG index tertiles. Moreover, heart rate, TIMI risk score for STEMI and the level of total TC, HDL-C, LDL-C, albumin, FBG and TG differed significantly across the TyG index tertiles. In contrast, no significant differences in infarct size, peak creatine kinase (CK), time from onset to arrival, Scr, radial approach, cardiogenic shock, left ventricular ejection fraction (LVEF), pharmacological agents and periprocedural complications were observed between the aforementioned groups.

Table 1

The baseline characteristics based on tertiles of the TyG index

Variable	T1 (n=493)	T2 (n=504)	T3 (n=494)	P value
Age (years)	61.74±11.63	64.09±12.27	65.92±12.51	<0.001
Male (n, %)	413 (83.8%)	396 (78.6%)	379 (76.7%)	0.017
Hypertension (n, %)	255 (51.7%)	276 (54.8%)	303 (61.3%)	0.008
Diabetes mellitus (n, %)	65 (13.2%)	89 (17.7%)	201 (40.1%)	<0.001
Stroke (n, %)	18 (3.7%)	33 (6.5%)	31 (6.3%)	0.055
Smoker (n, %)	221 (44.8%)	234 (46.4%)	237 (48.0%)	0.593
Alcohol drinker (n, %)	86 (17.4%)	86 (17.1%)	114 (23.1%)	0.022
SBP (mm Hg)	120 (109–135)	120 (110–135)	120 (110–138)	0.186
DBP (mm Hg)	74 (65–81)	75 (70–80)	76 (70–83)	0.127
Heart rate (bpm)	75 (65–85)	75 (65–85)	78 (68–89)	0.006
Infarcted region
Anterior AMI (n, %)	152 (30.8%)	146 (29.0%)	145 (29.3%)	0.606
Inferior AMI (n, %)	209 (42.4%)	199 (39.5%)	195 (39.5%)	0.559
Lateral-wall AMI (n, %)	12 (2.4%)	22 (4.4%)	16 (3.2%)	0.235
Posterior- wall AMI (n, %)	19 (3.9%)	14 (2.8%)	24 (4.9%)	0.204
Onset-arrival time (hours)	7.00 (3.54–16.00)	6.92 (4.00–18.00)	7.00 (3.60–17.13)	0.783
FPG (mmol/L)	5.72±3.55	6.14±1.80	8.60±4.35	<0.001
Albumin (g/L)	37.34±5.56	39.03±3.86	39.42±11.33	<0.001
Scr (umol/L)	79.15 (65.7–93)	77.4 (65–92.1)	77 (64.1–95.4)	0.763
TC (mmol/L)	3.83 (3.24–4.51）	4.23 (3.62–4.94）	4.55 (3.85–5.28）	<0.001
Triglycerides (mmol/L)	0.88 (0.71–1.08）	1.42 (1.19–1.66）	2.14 (1.59–2.85）	<0.001
HDL-C (mmol/L)	1.11±0.33	1.04±0.27	1.01±0.30	<0.001
LDL-C (mmol/L)	2.42±0.90	2.68±0.80	2.82±0.91	<0.001
Radial approach (n, %)	449 (91.1%)	447 (88.7%)	438 (88.7%)	0.365
Cardiogenic shock (n, %)	21 (4.3%)	30 (6.0%)	24 (4.9%)	0.463
TIMI risk score for STEMI	5.55±1.93	5.73±2.18	5.90±2.49	0.041
LVEF (%)	58.51±8.99	59.79±8.95	58.38±9.13	0.780
Medications
Antiplatelets
Aspirin (n, %)	440 (89.2%)	450 (89.3%)	438 (88.7%)	0.940
Clopidogrel (n, %)	250 (50.7%)	271 (53.8%)	263 (53.2%)	0.588
Ticagrelor (n, %)	162 (32.9%)	165 (32.7%)	172 (34.8%)	0.738
Beta-blockers (n, %)	348 (70.6%)	352 (69.8%)	342 (69.2%)	0.956
Nitrates (n, %)	423 (85.8%)	437 (86.7%)	425 (86.0%)	0.840
ACEI/ARB (n, %)	369 (74.8%)	363 (72.0%)	372 (75.3%)	0.328
Statins (n, %)	407 (82.6%)	416 (82.5%)	402 (81.4%)	0.960
Heparin/low molecular heparin (n, %)	400 (81.1%)	401 (79.6%)	393 (79.6%)	0.818
Periprocedural complications
Periprocedural stroke (n, %)	2 (0.4%)	3 (0.6%)	2 (0.4%)	0.879
Bleeding complications (n, %)	3 (0.6%)	6 (1.2%)	4 (0.8%)	0.604
Coronary artery perforation (n, %)	1 (0.2%)	0	1 (0.2%)	0.600

Data are expressed as mean±SD for normally distributed data, median (IQR) for abnormally distributed data and percentage (%) for categorical variables.

ACEI, angiotensin converting enzyme inhibitor; ALT, alanine aminotransferase; AMI, acute myocardial infarction; ARB, angiotensin receptor inhibitor; AST, aspartate aminotransferase; CK, creatine kinase; DBP, diastolic blood pressure; FBG, fasting blood glucose; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; LVEF, left ventricular ejection fraction; SBP, systolic blood pressure; Scr, serum creatinine; TC, total cholesterol; TIMI, Thrombolysis in Myocardial Infarction.

Association between the TyG index and the severity of coronary stenosis in patients with STEMI

The Spearman’s correlation study revealed a significantly but weakly positive association between the TyG index and GS (r=0.262, p<0.001) (online supplemental figure 2). According to univariate logistic regression analysis, age, hypertension, diabetes mellitus, LDL, Scr, FBG and the TyG index were identified as possible risk factors for a high GS (univariate p<0.05, table 2). FBG and TG, which make up the TyG index, were left out of the multivariate logistic regression model to prevent any potential interactions. The possible risk factors were incorporated into the multivariate model as variables after collinearity testing. And then eventually, three independent predictors of the high GS (GS >60) emerged in this series: age (OR 1.031, 95% CI 1.020 to 1.043, p<0.001), LDL-C (OR 1.267, 95% CI 1.085 to 1.479, p=0.003) and TYG (OR 2.003, 95% CI 1.633 to 2.458, p<0.001).

Table 2

Univariate and multivariate logistic regression analyses for predicting a high GS

Variables	Univariate analysis			Multivariate analysis
	Unadjusted OR	95% CI	P value	Adjusted OR	95% CI	P value
Age	1.024	1.014 to 1.035	<0.001	1.031	1.020 to 1.043	<0.001
Male	0.964	0.700 to 1.327	0.822
Hypertension	1.411	1.107 to 1.799	0.005	1.18	0.907 to 1.534	0.218
Diabetes mellitus	1.351	1.002 to 1.823	0.048	1.036	0.743 to 1.443	0.836
Stroke	1.143	0.624 to 2.096	0.665
Alcohol drinker	0.916	0.676 to 1.239	0.568
Smoker	1.055	0.829 to 1.342	0.665
Heart rate	1.008	1.000 to 1.016	0.051
SBP	1.001	0.998 to 1.004	0.625
DBP	1.005	0.996 to 1.015	0.277
FBG	1.048	1.001 to 1.097	0.047
TC	1.003	0.951 to 1.123	0.438
TG	0.965	0.881 to 1.058	0.449
HDL-C	1.054	0.701 to 1.586	0.801
LDL-C	1.298	1.120 to 1.505	0.001	1.267	1.085 to 1.479	0.003
Albumin	0.973	0.947 to 1.001	0.051
Scr	1.004	1.001 to 1.009	0.042	1.003	0.999 to 1.007	0.189
TYG	2.143	1.775 to 2.587	<0.001	2.003	1.633 to 2.458	<0.001

DBP, diastolic blood pressure; FBG, fasting blood glucose; GS, Gensini score; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; Scr, serum creatinine; TC, total cholesterol; TG, triglycerides; TYG, triglyceride-glucose.

Further construction of logistic regression models revealed a substantial correlation between the TyG score and the degree of coronary stenosis (p<0.001, table 3). The TyG index was strongly related to a high GS when examined as a continuous variable (OR 2.143, 95% CI 1.775 to 2.587, p<0.001). The risk of a high GS was 1.908 times higher (95% CI 1.421 to 2.562, p=0.001) and 2.167 times higher (95% CI 1.603 to 2.928, p<0.001) for the T2 and T3 groups, respectively, as compared with the T1 group. The TyG index as a continuous variable was an independent predictor of a high GS in model I following age, hypertension and diabetes mellitus adjustments (OR 2.278, 95% CI 1.873 to 2.770, p<0.001). The multivariate logistic regression analysis showed that the risk of a high GS was 1.990 times higher (95% CI 1.476 to 2.684, p=0.001) and 2.394 times higher (95% CI 1.759 to 3.259, p<0.001) in the T2 and T3 groups, respectively, when using the T1 group as a reference. After adjusting for other factors in model II, the TyG index as a continuous variable was still an independent factor for a high GS (OR 2.003, 95% CI 1.633 to 2.458, p<0.001). Using the T1 group as a reference, the ORs for the risk of a high GS in the T2 group and T3 group were 1.732 and 1.968, respectively. The trend test indicated that the upward trend had statistical significance (all p for trend <0.001).

Table 3

Associations between the TyG index and the severity of coronary stenosis

	Non-adjusted		Model I		Model II
	OR (95% CI)	P value	OR (95% CI)	P value	OR (95% CI)	P value
TyG	2.143 (1.775 to 2.587)	<0.001	2.278 (1.873 to 2.770)	<0.001	2.003 (1.633 to 2.458)	<0.001
T1	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
T2	1.908 (1.421 to 2.562)	0.001	1.990 (1.476 to 2.684)	0.001	1.732 (1.275 to 2.354)	<0.001
T3	2.167 (1.603 to 2.928)	<0.001	2.394 (1.759 to 3.259)	<0.001	1.968 (1.432 to 2.705)	<0.001
p for trend		<0.001		<0.001		<0.001

None means non-adjusted model. Model I was adjusted for age, hypertension and diabetes mellitus. Model II was adjusted for age, hypertension, diabetes mellitus, low-density lipoprotein cholesterol and serum creatinine.

TyG, triglyceride glucose.

The area under the curve (AUC) of the ROC curve of the TyG index as an indicator to predict a high GS in patients with STEMI was 0.668 (95% CI 0.635 to 0.700, p<0.001) (online supplemental figure 3). Furthermore, we used RCS to visualise the relationship between the TyG index and the severity of coronary stenosis. The RCS findings revealed a dose-response relationship between the TyG index and the risk of a high GS (non-linear p=0.159) (online supplemental figure 4).

Association between the TyG index and in-hospital mortality in patients with STEMI

The overall in-hospital mortality rate was 5.43% (n=81). Univariate and multivariate Cox regression analyses of predictors of in-hospital mortality among patients with STEMI who underwent PCI are presented in table 4. Age, gender, stroke history, heart rate, SBP, DBP, Scr, cardiogenic shock and TYG index were identified as risk variables for in-hospital mortality in patients with STEMI following PCI by univariate Cox regression (all p<0.05). After checking for collinearity, multivariable Cox regression was performed to explore the potential risk factors, and the results showed that the TyG index was an independent predictor of in-hospital mortality (HR 1.525, 95% CI 1.060 to 2.195, p<0.001). Table 5 shows the association between the TyG index as a categorical variable and the risk of in-hospital mortality after adjusting for potential confounding factors. After adjusting for age, gender, stroke history, SBP, DBP, Scr, heart rate and cardiogenic shock in model II, the risk for in-hospital mortality in the T3 group was 2.039 times higher (HR 2.039, 95% CI 1.159 to 3.587, p=0.003) using the T1 group as a reference. It was statistically significant that the risk of in-hospital mortality increased from the T1 group to the T3 group (all p for trend <0.05). According to Kaplan-Meier curves, patients with a higher TyG index had higher mortality (online supplemental figure 5).

Table 4

Univariate and multivariate Cox regression analyses of factors associated with in-hospital mortality

Variables	Univariate analysis			Multivariate analysis
	Unadjusted HR	95% CI	P value	Adjusted HR	95% CI	P value
Age	1.056	1.038 to 1.074	<0.001	1.050	1.022 to 1.079	<0.001
Male	1.791	1.216 to 2.638	0.003	1.124	0.585 to 2.157	0.726
Hypertension	1.28	0.968 to 2.011	0.074
Diabetes mellitus	1.276	0.857 to 1.899	0.230
Stroke	2.323	1.336 to 4.038	0.003	1.582	0.767 to 3.263	0.214
Alcohol drinker	0.601	0.335 to 1.081	0.089
Smoker	0.332	0.213 to 0.518	<0.001
Heart rate	1.004	1.002 to 1.006	<0.001	1.002	0.998 to 1.006	0.398
SBP	0.976	0.967 to 0.985	<0.001	0.989	0.971 to 1.007	0.211
DBP	0.965	0.952 to 0.978	<0.001	0.962	0.945 to 0.981	<0.001
TG	0.987	0.814 to 1.198	0.897
HDL	0.616	0.259 to 1.464	0.273
LDL	0.952	0.721 to 1.256	0.726
Scr	1.003	1.002 to 1.004	<0.001	1.004	1.002 to 1.006	<0.001
Cardiogenic shock	3.303	1.694 to 6.427	<0.001	4.540	1.916 to 10.756	<0.001
PCI with radial approach	0.932	0.428 to 2.027	0.859
PCI during night shift	1.382	0.881 to 2.168	0.159
TYG	1.543	1.138 to 2.092	0.005	1.525	1.060 to 2.195	<0.001

Abbreviations as shown in table 1.

Table 5

Associations between the TyG index and in-hospital mortality

	Non-adjusted		Model I		Model II
	HR (95% CI）	P value	HR (95% CI）	P value	HR (95% CI)	P value
TYG	1.543 (1.138 to 2.092)	p=0.005	1.735 (1.276 to 2.359)	<0.001	1.525 (1.060 to 2.195)	0.023
T1	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
T2	1.048 (0.580 to 1.895)	0.605	1.175 (0.649 to 2.216)	0.594	1.786 (0.986 to 3.235)	0.056
T3	1.751 (1.024 to 2.992)	0.040	2.230 (1.295 to 3.841)	0.004	2.039 (1.159 to 3.587)	0.003
p for trend		0.031		0.004		0.008

Abbreviation as shown in table 3.

None means non-adjusted model. Model I was adjusted for age and gender. Model II was adjusted for age, gender, stroke, systolic blood pressure, diastolic blood pressure, heart rate, serum creatinine and cardiogenic shock.

Discussion

In this multicentre, retrospective, observational study, the impacts of the TyG index on the severity of coronary stenosis and in-hospital mortality were assessed in patients with STEMI undergoing PCI. The findings of our study demonstrated that higher TyG index levels were independently associated with more severe coronary stenosis and an increased risk of in-hospital mortality among patients with STEMI undergoing PCI, irrespective of their diabetes mellitus status.

The perioperative complication rate was also evaluated in the present study, revealing an incidence of coronary artery perforation of 0.13%, consistent with previous research findings.¹⁵ Moreover, the previous studies have demonstrated a significant association between mortality in patients with STEMI and the use of radial approach for PCI as well as performing PCI during the night shift.^{16 17} The findings of our study aligned with these prior results, although statistical significance had not yet been achieved, potentially due to the limited size of the database. Additionally, our results showed that cardiogenic shock is a strong predictor of in-hospital mortality, which was consistent with previous cohort studies.¹⁸

Impaired insulin sensitivity and abnormalities in glucose absorption and utilisation characterise IR, with insulin-mediated nitric oxide (NO) production improving blood flow and glucose disposal.⁴ In states of IR, a further crucial factor in the development of CVD is a decreased sensitivity to the usual vascular activities of insulin, particularly a decreased NO production. IR causes CVD in different populations primarily through imbalances in glucose and lipid metabolism, increases in vascular stiffness and endothelial dysfunction, and triggers oxidative stress and inflammatory response.^19–22 In addition, IR has been proven to be a risk factor for CVD.^{23 24} However, the hyperinsulinaemic-euglycaemic clamp (HIEC) technique, which is sophisticated, expensive, time-consuming and not available for use in routine clinical practice, is required for the gold standard for quantifying IR.²⁵ Homeostasis model assessment of IR (HOMA-IR) is a comparatively extensive approach for testing IR because it is strongly correlated with HIEC.^{26 27} Nevertheless, the determination of insulin concentration is not routine, which makes HOMA-IR not appropriate for large-scale clinical practice. The TyG index, which is reliable, easily and inexpensively obtained in clinical practice, has been proven to have a strong correlation with the HIEC and HOMA-IR.^28–30 According to previous studies, it has even been demonstrated that the TyG index is superior to HOMA-IR for determining IR in both individuals with and without diabetes.^{31 32}

The TyG index is a reliable predictor of the advancement of CAC, regardless of other traditional CVD risk factors, according to a large-scale retrospective longitudinal investigation conducted in Korea.³³ In addition, it has been reported that the number and severity of arterial stenoses among individuals with type 2 diabetes are correlated with the TyG index, which could help identify those with a high risk of developing coronary artery stenosis.⁸ Recently, the findings of Wang et al showed that a higher TyG index was strongly linked to a higher risk of multivessel CAD, particularly in individuals with pre-diabetes mellitus.³⁴ In addition, Xiong et al observed that in patients with acute coronary syndrome (ACS), there was a significant correlation between the TyG index and the Synergy Between Percutaneous Coronary Intervention score (SYNTAX score).¹⁰ Another prospective study conducted by Kurtul et al revealed a significant correlation between the SYNTAX score and the incidence of complications such as diabetic retinopathy in patients diagnosed with type 2 diabetes mellitus.³⁵ The TyG index’s impact on the degree of coronary stenosis and clinical outcomes in patients with STEMI undergoing PCI, however, is not currently known. The GS system, which is unbiased and trustworthy to assess the severity of CAD, was employed in the current investigation to gauge the severity of coronary lesions.³⁶ The present study demonstrated that the TyG index, a non-invasive measure, effectively predicted the severity of coronary stenosis in patients with STEMI as assessed by GS.

There was insufficient illustration of the precise mechanism that might explain the link between the TyG index levels and the risk of STEMI. Our results showed that patients with a higher IR, as measured by the TyG index, are more likely to have serious coronary lesions and have a poor prognosis. Mounting evidence showed that improving IR through medication was a promising option for people with diabetes who were at risk of having serious adverse cardiac events.^{37 38} It has been demonstrated that medications that target IR, such as glucagon-like peptide-1 and thiazolidinediones, lower the risk of cardiovascular events and mortality.^39–41 Therefore, treatment for IR may help to improve cardiovascular outcomes and coronary lesions.

The main strengths of the present study include a relatively large number of patients with STEMI and the extensive range of available laboratory indexes, but there are still some limitations. First, this study only enrolled the Chinese population, which may not reflect the whole cohort. In addition, due to the limitations of a retrospective design, all measurements and laboratory parameters were evaluated only once over the follow-up period. Furthermore, prospective cohort studies are required to confirm the clinical significance of this finding.

Conclusions

In conclusion, the current study showed that, in patients with STEMI undergoing PCI, a significant relationship between TyG index and GS indicated that TyG index could be useful for predicting the prevalence of a greater severity of coronary stenosis, regardless of diabetes mellitus status. Additionally, the TyG index, an independent risk factor for in-hospital mortality in patients with STEMI, may aid in risk stratification and detecting high-risk patients requiring aggressive interventions.

Data availability statement

Data are available upon reasonable request. Data are available from the corresponding author on reasonable request.

Ethics statements

Patient consent for publication

Consent obtained directly from patient(s).

Ethics approval

This study involves human participants and was approved by the medical ethics committee of Affiliated Hospital of Nanjing University of Chinese Medicine (2022NL-111-02). Participants gave informed consent to participate in the study before taking part.

Footnote

Contributors Conceptualisation: XLu. Formal analysis: XLu, YC, XZ and PY. Investigation: XLu, XLin, HM and XC. Software: WC. Writing—original draft: XLu and YC. Writing—review and editing: XC. XC is responsible for the overall content as the guarantor.

Funding This study was partly supported by a grant to the National Natural Science Foundation of China (grant no. 81,973,824), the subject of Jiangsu Province Hospital of Chinese Medicine (grant no. Y2021CX29) and Jiangsu Provincial Cadre Health Research Project (grant no. BJ21024).

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

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