Correlation between the TyG-BMI index and carotid plaque characteristics in middle-aged and elderly patients with acute myocardial infarction

Abstract

OBJECTIVE:

The aim of this study is to investigate the correlation between the triglyceride-glucose-body mass index (TyG-BMI) and the characteristics of various carotid plaques in middle-aged and elderly patients with acute myocardial infarction (AMI).

METHODS:

A retrospective study was conducted on 380 patients with AMI hospitalized in the Cardiology Department of Kaifeng Central Hospital. Based on carotid ultrasound results, patients were divided into the following two groups: the stable plaque group and the unstable plaque group. Additionally, a control group comprising 380 healthy individuals visiting the hospital’s physical examination center during the same timeframe was established. Fasting venous blood samples were collected from all participants to measure blood glucose and triglyceride. The baseline TyG-BMI index was calculated using the formula Ln [fasting triglyceride (mg/dL)×fasting blood glucose (mg/dL)/2]×BMI. The correlation between different plaque groups and the TyG-BMI index was analyzed.

RESULTS:

The TyG-BMI index was significantly higher in the unstable plaque group compared to the stable plaque group, with values of 252.81±29.99 and 201.92±28.72, respectively (P = 0.034). Spearman’s correlation analysis showed a positive correlation between the instability of carotid plaques and the TyG-BMI index in patients with AMI (r = 0.521, P = 0.003). Logistic regression analysis indicated that the TyG-BMI index was an important risk factor for unstable carotid plaques in patients with AMI (OR = 2.691, 95% CI: 1.169–4.123).

CONCLUSION:

The findings of this study suggest that an elevated TyG-BMI index significantly increases the risk of unstable carotid plaques in patients with AMI, making it an important risk factor for carotid plaque instability.

Keywords

Atherosclerosis acute myocardial infarction carotid artery TyG-BMI index unstable plaques

1 Introduction

Coronary artery disease (CAD) remains a prominent cause of mortality, with an escalating incidence rate [1], significantly impacting public health and contributing to rising healthcare expenditures and societal burdens both in China and internationally [2]. Despite ongoing optimization of revascularization and pharmacological treatment strategies, recurrent cardiovascular events and complications remain prevalent [3].

Therefore, early prevention and detection of the disease are crucial. Improving risk stratification, early identification of high-risk individuals, and early intervention are of significant clinical importance for promoting the recovery of patients with CAD. This is a considerable challenge in the field of cardiovascular medicine.

The triglyceride-glucose-body mass index (TyG-BMI index) is recognized as a reliable surrogate marker for assessing insulin resistance (IR) [3]. While existing research has linked the triglyceride-glucose-body mass index (TyG-BMI) to various diseases, its association with the characteristics of carotid plaques in patients experiencing atherosclerosis-induced acute myocardial infarction (AMI) remains underexplored. This study aims to explore the potential relationship between the TyG-BMI index and carotid plaque characteristics to evaluate its utility as an indicator for risk stratification in patients with AMI.

2 Participants and methods

2.1 Participants

From September 2020 to August 2022, a cohort of 380 consecutive patients diagnosed with type I AMI and hospitalized at Kaifeng Central Hospital were enrolled and assigned to the AMI group. This group comprised 198 males (52.1%) and 182 females (47.9%), with an average age of 64.59±5.07 years. All patients had their infarct-related artery identified through coronary angiography (CAG) and were diagnosed in accordance with the 2018 Universal Definition of Myocardial Infarction [3]. Patients with non-obstructive myocardial infarction, confirmed by negative CAG results were excluded.

During the same period, 380 individuals who underwent health examinations at the hospital and were confirmed by CAG or coronary CTA to have no significant coronary artery stenosis were enrolled and assigned to the control group. This group included 193 males (50.7%) and 187 females (49.3%), with an average age of 62.74±4.88 years.

Exclusion criteria: (1) Patients with valvular heart disease; (2) with severe acute or chronic heart failure; (3) with severe myocardial or pericardial disease; (4) with severe infections; (5) with liver insufficiency or on dialysis; (6) with active malignancies; (7) with hyperthyroidism or hypothyroidism; and (8) with severe immune system diseases.

There were no statistically significant differences in age and gender between the two groups (P > 0.05).

All research information of participants used in this study was de-identified. The ethics committee of our institution granted an exemption from ethical approval.

2.2 Data collection

For the AMI group, baseline data were obtained by reviewing archived electronic medical records, including gender, age, weight, height, history of hypertension, diabetes, hyperlipidemia, stroke, smoking, and family history. Biochemical parameters recorded were the first measurements taken after admission, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG). Carotid ultrasound indicators recorded during hospitalization included carotid intima-media thickness (IMT), and the location, morphology, and echogenicity of carotid plaques.

For the control group, data on serum lipids and carotid color Doppler ultrasound results were collected from health examinations performed at the health examination center of the hospital during the same period.

2.3 Data collection methods

The TyG-BMI index for both groups was calculated using the formula Ln [fasting TG (mg/dL)×fasting blood glucose (mg/dL)/2]×BMI. Comprehensive carotid color doppler ultrasound examinations were conducted. For the control group, fasting blood samples were collected and tested, and color doppler ultrasound examinations were completed at the health examination center of the hospital. Serum testing for both groups was performed in the unified biochemistry lab, and all color doppler ultrasound examinations were conducted in the color Doppler ultrasound room of the hospital to ensure consistent quality of the results.

2.4 Ultrasound examination

A GE LOGIQ three-dimensional color Doppler ultrasound machine, with a probe frequency of 10 MHz, was used to evaluate the characteristics of carotid plaques.

(1) Carotid plaques: IMT was measured as the vertical distance from the inner surface of the lumen intima to the boundary between the vascular media and adventitia. IMT < 1 mm was considered normal; 1 mm≤IMT≤1.2 mm indicated intimal thickening and the presence of atherosclerosis; IMT > 1.2 mm indicated plaque formation.

(2) Plaque characteristics: Plaques were classified by ultrasound based on their echogenicity.

a) Hypoechoic soft plaques: Lipid-rich plaques with homogeneously low echogenicity.

b) Calcified plaques: High-echogenicity plaques or those casting posterior acoustic shadows on imaging, indicating hard plaques.

c) Mixed plaques: Plaques with heterogeneous echogenicity, indicating ulcerated plaques. Soft plaques and mixed plaques were defined as unstable plaques, while calcified plaques or hard plaques were defined as stable plaques.

2.5 Statistical methods

Statistical analysis was performed using SPSS 26.0 software package. Measurement data are expressed as mean±standard deviation ( $\bar{x}$ ±s). The independent sample t-test was used to compare means between two groups, and analysis of variance was used for comparisons among multiple groups, with Bonferroni correction applied for pairwise comparisons. A binary logistic regression model was used for multivariate analysis. Spearman’s correlation analysis was employed to examine the relationships between variables. P < 0.05 was considered statistically significant.

3 Results

3.1 Comparison of TyG index and TyG-BMI index between the AMI group and the control group

TyG index and TyG-BMI index were higher in the AMI group than in the control group. The TyG-BMI index was (208.17±21.90) mmol/L in the AMI group and (188.62±18.72) mmol/L in the control group (P = 0.014). See Table 1 for details.

Table 1
Comparison of the levels of serum lipids, TyG index, and TyG BMI index between the AMI group and the health controls

Group TG/(mmol·L-1) LDL-C/(mmol·L-1) HDL-C/(mmol·L-1) TyG index TyG-BMI index

Control (n = 380) 1. 561±0. 58 2. 29±0. 71 1. 39±0. 45 6. 24±1.12 188. 84±20. 80

AMI (n = 380) 1. 84±0. 86 2. 53±0. 95 1. 13±0. 39 6.38±1.73 201. 17±19. 03

t 4.322 4. 047 –12. 445 1.324 4. 973

P 0. 044 <0. 001 <0. 001 0.186 <0. 001

Group	TG/(mmol·L-1)	LDL-C/(mmol·L-1)	HDL-C/(mmol·L-1)	TyG index	TyG-BMI index
Control (n = 380)	1. 561±0. 58	2. 29±0. 71	1. 39±0. 45	6. 24±1.12	188. 84±20. 80
AMI (n = 380)	1. 84±0. 86	2. 53±0. 95	1. 13±0. 39	6.38±1.73	201. 17±19. 03
t	4.322	4. 047	–12. 445	1.324	4. 973
P	0. 044	<0. 001	<0. 001	0.186	<0. 001

TG: triglyceride; LDL-C: low density lipoprotein- cholesterol; HDL-C: high density lipoprotein- cholesterol; TyG index; Ln[fasting triglycerides (mg/dL)×Fasting blood glucose (mg/dL)/2].

3.2 Comparison of TyG index and TyG-BMI index among subgroups of patients with AMI

Based on carotid ultrasound evaluation of plaque characteristics, 156 patients (41.1%) in the AMI group were identified with stable plaques (stable plaque group), and 189 patients (49.7%) were identified with unstable plaques (unstable plaque group). Compared to the non-plaque group (35 patients, 9.2%), both the stable and unstable plaque groups had significantly higher TyG and TyG-BMI indices (P < 0.05).

Pairwise comparisons showed no significant difference in TyG index between the stable and unstable plaque groups (P = 0.072), but the TyG-BMI index was significantly higher in the unstable plaque group than the stable plaque group (P = 0.008). See Table 2 for details.

Table 2
Test results of TyG index, and TyG BMI index in each group

Group TG/(mmol·L-1) LDL-C/(mmol·L-1) HDL-C/(mmol·L-1) TyG index TyG BMI index

Non-plaques (n = 86) 1.29±0.92 2.37±1.06 1.15±0.27 6.63±1.90 192.48±0.83

Stable plaques (n = 156) 1.57±1.15^* 2.76±0.79^* 1.13±0.28 6.75±1.78 201.92±28.72^

Unstable plaques (n = 158) 1.95±1.38^ 3.05±0.9^▴ 1.13±0.52 6.91±1.85 252.81±29.99^▴

F 9.07 3. 36 0. 08 2.21 7. 16

P <0.001 <0. 001 0. 91 0.11 0. 008

Group	TG/(mmol·L-1)	LDL-C/(mmol·L-1)	HDL-C/(mmol·L-1)	TyG index	TyG BMI index
Non-plaques (n = 86)	1.29±0.92	2.37±1.06	1.15±0.27	6.63±1.90	192.48±0.83
Stable plaques (n = 156)	1.57±1.15^*	2.76±0.79^*	1.13±0.28	6.75±1.78	201.92±28.72^**
Unstable plaques (n = 158)	1.95±1.38^**	3.05±0.9^**▴	1.13±0.52	6.91±1.85	252.81±29.99^**▴
F	9.07	3. 36	0. 08	2.21	7. 16
P	<0.001	<0. 001	0. 91	0.11	0. 008

^*P < 0.05, ^**P < 0.01 vs non-plaques group; ▴P < 0.05 vs stable plaques; TG: triglycerid; LDL-C: low density lipoprotein- cholesterol; TyG index:Ln [fasting triglycerides (mg/dL)]×Fasting blood glucose (mg/dL)/2]; TyG BMI index: Ln [fasting triglycerides (mg/dL)].

3.3 Relationship between TyG-BMI index and characteristics of carotid plaques

Spearman’s correlation analysis showed a positive correlation between the unstable carotid plaques and the TyG-BMI index levels in patients with AMI (r = 0.521, P = 0.003).

3.4 Multivariate analysis of the relationship between serum TyG-BMI index and unstable carotid plaques

With the characteristics of carotid plaques as the dependent variable (unstable plaques = 1, stable plaques = 0), and the TyG-BMI index as the independent variable (which was statistically significant in the previous analyses), a logistic regression analysis was performed. The findings demonstrated that an elevated TyG-BMI index was significantly associated with an increased risk of unstable carotid plaques in patients with AMI (P = 0.003), with a 95% confidence interval (CI) of 1.169–4.123, and an odds ratio (OR) of 2.691, making it an important risk factor for unstable carotid plaques. See Table 3 for details.

Table 3
Binary Logistic regression analysis of riskfactors of carotid artery unstable plaque

Risk factors B Wald P OR 95% CI

TyG index –0.161 0.402 0.524 1.842 0.715–1.503

TyG BMI index 1.354 27.072 0.003 2.691 1.169–4.123

Risk factors	B	Wald	P	OR	95% CI
TyG index	–0.161	0.402	0.524	1.842	0.715–1.503
TyG BMI index	1.354	27.072	0.003	2.691	1.169–4.123

LDL-C: low density lipoprotein- cholesterol; HDL-C:high density lipoprotein- cholesterol. TyG index:TyG index; Ln [fasting triglycerides (mg/dL)]×Fasting blood glucose (mg/dL)/2].

4 Discussion

Cardiovascular disease (CVD) represents a prominent cause of morbidity and mortality globally, presenting substantial public health implications and economic burdens for affected individuals. Although several risk factors for CVD have been identified, including age, male gender, obesity, hypertension, hypercholesterolemia, and diabetes, recent studies suggest that some individuals without these risk factors may still develop CVD [1 , 4].

Previous research has established a positive correlation between the TyG index and cardiovascular and cerebrovascular diseases. However, it is noteworthy that current risk assessment models have yet to integrate the TyG index as a risk factor. Despite technological advancements and the widespread adoption of primary and secondary preventive strategies, the risk of recurrent adverse cardiovascular events remains elevated in patients with CVD. Identifying individuals at an early stage of CVD is vital for enhancing risk stratification and optimizing the management of chronic CVD.

In AMI treatment, early detection and preventive measures play a pivotal role. However, it is worth noting that existing cardiovascular risk prediction models in China exhibit suboptimal specificity. Consequently, there is a critical need to identify novel susceptibility factors specific to the Chinese population. These factors can inform precise prevention strategies and guide effective treatment approaches for cardiovascular disease CVD.

The TyG index is used to identify IR; however, its relatively low specificity may result in a significant number of false-positive results, thereby constraining its extensive use in IR screening [4]. Nevertheless, numerous clinical studies over the years have consistently validated the TyG index as a reliable marker for evaluating IR in high-risk populations [5 –8]. Notably, it outperforms other indicators when predicting high-risk diabetes populations. It is not only associated with diabetes but also with conditions such as stroke, obesity, hypertension, dyslipidemia, metabolic syndrome, and coronary atherosclerosis [7 –11].

As an effective surrogate marker for IR, TyG index has increasingly been linked to the development of CVD and poor prognosis. Since 2016, multiple studies have consistently demonstrated a positive association between the TyG index and cardiovascular events, including heart failure in coronary heart disease, cerebrovascular disease, and peripheral artery disease, independent of confounding factors [5 , 13]. The relationship between the TyG index and various types of CVDs continues to be an area of active exploration. Clinicians often first consider fasting blood glucose and TG levels when screening individuals at high risk for CVD. However, questions remain regarding how TyG index enhances the predictive values of TG and fasting blood glucose levels.

Currently, there is a lack of comparative studies on the predictive values of TyG index versus TG and fasting blood glucose. Additionally, CVD encompasses a series of dynamic and progressive metabolic disorders, and acute conditions such as myocardial infarction may cause stress-induced hyperglycemia, potentially affecting the diagnostic or predictive value of TyG index based on its formula [14, 15]. In most studies, TG and fasting blood glucose levels are measured only at baseline, without considering their changes over time, which may introduce potential analytical bias. Therefore, measuring TyG index only at baseline does not reflect the longitudinal association between TyG index and CVD risk over time.

Currently, most methods for assessing IR are limited in their practical clinical application and cannot be widely employed. However, TyG index, calculated from fasting plasma TG and glucose levels, has been shown in numerous studies to be closely associated with the gold standard for evaluating IR, the hyperinsulinemic euglycemic clamp (HEC) test. Nevertheless, the role of the TyG index in assessing the risk of cardiovascular events in patients with CAD is not clearly defined, prompting the ongoing search for new indicators to improve early disease risk prediction. Several studies have further confirmed the correlation between elevated derivative indices, such as TyG-BMI value, and increased incidence of adverse cardiovascular events [16 –20].

The TyG-BMI index is related to the pathophysiological progression of diseases such as atherosclerosis, diabetes, and peripheral vascular atherosclerosis, with the progression of peripheral arterial atherosclerosis further exacerbating IR [21] and aggravating the occurrence and development of diabetic complications such as diabetic peripheral vascular disease and diabetic nephropathy. IR is mainly associated with the BMI [22]. The combination of TyG and BMI (or the TyG-BMI index) can better reflect the relationship with atherosclerosis as compared with TyG index alone. Additionally, cross-sectional studies have reported correlations between the TyG-BMI index and ischemic stroke [23].

The TyG-BMI index exhibits a potential correlation with the occurrence of atherosclerosis in patients with CAD. However, existing research remains limited regarding the association between the TyG-BMI index and the characteristics of carotid atherosclerotic plaques in patients with AMI.

Notably, the results of this study suggest a link between the TyG-BMI index and unstable carotid plaques in patients with AMI. An elevated TyG-BMI index in patients with AMI significantly augments the risk of unstable carotid plaques, thereby serving as a crucial risk factor associated with their development. The risk of thrombotic events can cause acute cardiovascular and cerebrovascular events, posing serious harm to the human body [24]. Future investigations will explore the integration of the TyG-BMI index with conventional atherosclerotic cardiovascular disease risk indicators. The objective is to assess whether this combined approach enhances early risk stratification in patients with AMI. Some indexes in the blood may increase during the atherosclerosis period, Atherosclerosis or stenosis of the carotid artery is reflected in blood indicators and is associated with cerebral infarction and other diseases [25 –28]. By achieving early identification, targeted intervention, prevention, and effective disease management, this approach aims to optimize patient outcomes and reduce complications. The predictive value of the TyG-BMI index for plaque characteristics provides more comprehensive evidence for better prediction and intervention in unstable plaques in patients with coronary heart disease, facilitating the early development of clinical treatment plans.

Footnotes

Acknowledgments

We are particularly grateful to all the people who have given us help on our article.

Funding

No external funding received to conduct this study.

Conflict of interest

The authors declare that they have no competing interests.

Ethics approval and consent to participate

This study was conducted as a retrospective observational analysis, designed with rigorous scientific principles. All participant data used in this research were anonymized and could not be directly identified. The study protocol was clearly defined, and after thorough review, the ethics committee of our institution granted an exemption from ethical approval.

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Correlation between the TyG-BMI index and carotid plaque characteristics in middle-aged and elderly patients with acute myocardial infarction

Abstract

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1 Introduction

2 Participants and methods

2.1 Participants

2.2 Data collection

2.3 Data collection methods

2.4 Ultrasound examination

2.5 Statistical methods

3 Results

3.1 Comparison of TyG index and TyG-BMI index between the AMI group and the control group

3.4 Multivariate analysis of the relationship between serum TyG-BMI index and unstable carotid plaques

Table 3 Binary Logistic regression analysis of riskfactors of carotid artery unstable plaque Risk factors B Wald P OR 95% CI TyG index –0.161 0.402 0.524 1.842 0.715–1.503 TyG BMI index 1.354 27.072 0.003 2.691 1.169–4.123

Footnotes

Acknowledgments

Funding

Conflict of interest

Ethics approval and consent to participate

References

Table 3
Binary Logistic regression analysis of riskfactors of carotid artery unstable plaque

Risk factors B Wald P OR 95% CI

TyG index –0.161 0.402 0.524 1.842 0.715–1.503

TyG BMI index 1.354 27.072 0.003 2.691 1.169–4.123