The role of TyG index in predicting low left ventricular ejection fraction after acute coronary syndrome

ABSTRACT

Aims

This study aimed to investigate whether triglyceride-glucose (TyG) index and other clinical and demographic parameters are associated with left ventricular ejection fraction (LVEF) following an acute coronary syndrome (ACS) event.

Methods & Results

This retrospective cohort study included patients hospitalized with a diagnosis of ACS. The TyG index was calculated using the formula: TyG = ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. A total of 2,135 patients were included in the study (mean age: 57.49 ± 11.45 years, 78.64% male). Multivariable logistic regression revealed that mildly reduced or reduced LVEF was associated with immigrant population (p = 0.004), diabetes mellitus (p = 0.017), previous coronary artery disease (CAD) (p < 0.001), ST-elevation myocardial infarction (STEMI) (p < 0.001) and high (≥4.95) TyG index (p < 0.001). Reduced LVEF (≤40%) was independently associated with an immigrant status (p = 0.031), previous CAD (p = 0.001), peripheral artery disease (p = 0.038), renal diseases (p = 0.011), STEMI (p < 0.001) and high (≥5.10) TyG index (p < 0.001).

Conclusions

The TyG index shows potential as an independent risk factor for low LVEF after ACS.However, its relatively low sensitivity and specificity suggest that it may have a supportive role in risk stratification. Further research is needed to confirm its utility as a reliable prognostic marker for heart failure in ACS patients.

1. Introduction

Acute coronary syndrome (ACS) is a leading cause of mortality [1]. Collectively known as coronary artery disease (CAD), the three major manifestations of this syndrome are ST-elevation myocardial infarction (STEMI), non-ST-elevation myocardial infarction (NSTEMI), and unstable angina pectoris (USAP) [2,3]. These conditions result from an atherosclerotic plaque rupture or erosion, leading to reduced coronary blood flow and myocardial damage, which can impair heart function [1,4]. In ACS events causing considerable loss of myocardial tissue, left ventricular ejection fraction (LVEF) is decreased, which might lead to heart failure (HF) [5,6]. The development of HF worsens quality of life and both short- and long-term prognosis [5,7,8]. Approximately 10–20% of ACS cases lead to acute HF; however, determining the extent of functional loss is difficult in the early post-ACS period without accurate measurement of LVEF [7,8]. Since LVEF is a key indicator of heart function, its decline following an ACS event is not just a short-term concern but also a predictor of long-term complications, including heart failure and mortality. Even a mild reduction in LVEF can significantly impact a patient’s prognosis, affecting their ability to perform daily activities and increasing their dependency on long-term medical care. Understanding the factors that contribute to LVEF deterioration is crucial for improving post-ACS patient management.

One of the biggest challenges in managing ACS is predicting how the heart will recover after the initial event. While imaging techniques such as echocardiography provide valuable insight into cardiac function, they are not always available for immediate assessment in all healthcare settings. Additionally, LVEF decline may not always be immediately evident, making it essential to explore alternative markers that could offer earlier risk stratification.

The triglyceride-glucose (TyG) index has recently gained attention as a marker of insulin resistance, calculated from fasting triglyceride and glucose levels. Insulin resistance is increasingly recognized as a contributor to cardiovascular disease progression, especially owing to the established impacts of oxidative stress, metabolic syndrome and type 2 diabetes [9,10]. Traditional risk factors such as hypertension, diabetes, and obesity have long been associated with cardiovascular disease progression. However, recent research suggests that metabolic health plays a much larger role than previously thought. Insulin resistance, in particular, has been linked to worse cardiac outcomes, highlighting the need for simple yet effective markers that can reflect both metabolic dysfunction and cardiovascular risk.

Unlike traditional methods for assessing insulin resistance, such as the HOMA-IR, the TyG index offers a simpler, more cost-effective approach with demonstrated reliability in predicting cardiovascular disease outcomes [10–12]. Unlike more complex measures of insulin resistance, such as the homeostatic model assessment of insulin resistance (HOMA-IR), the TyG index is easy to obtain from routine blood tests, making it a practical and cost-effective option for large-scale clinical use. Given the increasing burden of metabolic syndrome and type 2 diabetes in ACS patients, the ability to assess metabolic risk using a simple formula could provide significant advantages in early diagnosis and intervention. TyG has been associated with increased risks for adverse cardiovascular events and HF development [13–15]. In ACS, elevated TyG levels could reflect a heightened risk of post-event complications, including HF, since it could reflect metabolic dysfunctions on a systemic scale [1,10,16–21]. While the association between metabolic dysfunction and cardiovascular disease is well recognized, its direct impact on post-ACS LVEF is still not fully understood. Investigating this relationship could help bridge the gap between metabolic risk assessment and cardiac prognosis, providing clinicians with additional tools to improve patient outcomes.

We hypothesize that higher TyG values will correlate with lower LVEF after ACS, indicating impaired heart function, and potentially, poorer prognosis. Therefore, we aimed to investigate the relationship between the TyG index, selected clinical and demographic parameters, and LVEF in ACS patients-measured acutely during the first week post-event. By better understanding how metabolic markers such as the TyG index relate to heart function after ACS, we may be able to refine existing risk models and develop more personalized strategies for monitoring and treating high-risk patients. This study aims to explore the potential of the TyG index as a predictive tool for LVEF decline, offering new insights into the intersection of metabolic health and cardiac recovery.

2. Materials and methods

2.1. Study plan and conduct

This retrospective cohort study was conducted at the Coronary Intensive Care Unit of the Cardiology Department at Haseki Education and Research Hospital. Data were collected from patients admitted with ACS between January 2021 and December 2022.

Patients eligible for inclusion were those aged between 18 and 90 years who were admitted to the coronary care unit with a diagnosis of ACS. Exclusion criteria included patients with pancreatic disease, liver failure, or acute infections, as indicated in hospital records. In addition, patients requiring urgent surgery for ACS, those who died before LVEF could be measured, and patients who declined treatment or could not undergo follow-up procedures were excluded from the study group.

The primary outcome of the study was the relationship between baseline TyG level and LVEF one week after the ACS event. The secondary outcome was the relationship between other clinical and demographic parameters and LVEF.

The study was conducted in compliance with the Declaration of Helsinki and approved by the institutional ethics committee of Haseki Education and Research Hospital, Istanbul, Turkey (Decision date: 23 May 2024, decision no: 14–2024).

2.2. Data collection

Relevant medical records were reviewed to extract demographic data (age and sex), body mass index (BMI), smoking status (classified as Non-smokers, Ex-smokers, Passive smokers, Active smokers), immigration status, comorbidities (hypertension, diabetes mellitus, hyperlipidemia, etc.), ACS subtype, cardiac echocardiography findings after ACS, laboratory findings and mortality status. Laboratory values, including fasting blood glucose and triglyceride levels, were collected from blood samples taken between 07:00 and 10:00 AM after an 8–10 hour fasting period on the first day of admission. All laboratory tests were performed in certified laboratories according to standardized procedures. The TyG index was calculated using the formula: TyG = ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2] [22].

2.3. Disease management and classification

ACS diagnosis was established based on clinical presentation, ECG changes, and cardiac biomarker levels. Patients were categorized into one of three ACS types: USAP, NSTEMI or STEMI, in line with American College of Cardiology (ACC) guidelines [2].

For left ventricular function assessment, echocardiography was used to measure LVEF during the first week of admission. Based on LVEF values, patients were categorized into three groups to evaluate cardiac function according to the 2022 ACC/American Heart Association/Heart Failure Society of America Guideline [5]: Preserved LVEF (≥50%): Reflects normal systolic function with no or minimal impairment in left ventricular contraction; Mildly Reduced LVEF (41–49%): Indicates mild systolic dysfunction, suggesting some impairment in the heart’s ability to pump blood effectively; Reduced LVEF (≤40%): Signifies moderate to severe systolic dysfunction, typically associated with an increased risk of adverse cardiovascular outcomes.

2.4. Statistical analysis

All analyses were performed on IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp., Armonk, NY, USA). Histogram and Q-Q plots were used to determine whether variables are normally distributed. Descriptive statistics are presented using mean ± standard deviation for normally distributed continuous variables, median (25th percentile − 75th percentile) for non-normally distributed continuous variables and frequency (percentage) for categorical variables. Normally distributed variables were analyzed using one-way analysis of variance (ANOVA). Non-normally distributed variables were analyzed using Kruskal Wallis test. Categorical variables were analyzed using chi-square test or Fisher-Freeman-Halton test. Pairwise comparisons were adjusted using Bonferroni correction method. LVEF prediction performance of TyG index were assessed using receiver operating characteristic (ROC) curve analysis. Optimal cutoff points were determined using Youden’s index. Relationships between continuous variables were evaluated using Spearman correlation coefficient. Logistic regression analyses were performed to determine significant factors independently associated with LVEF. Variables were analyzed using univariable logistic regression analysis and statistically significant variables were included to multivariable logistic regression models.

3. Results

A total of 2,135 patients were included in the study, with a mean age of 57.49 ± 11.45 years; 78.64% (1,679) were male. The median LVEF was 50 (interquartile range 45–60). There were 1,351 patients (63.28%) in the preserved LVEF group, 399 (18.69%) in the mildly reduced LVEF group, and 385 (18.03%) in the reduced LVEF group. There were no significant differences in age (p = 0.644) or sex distribution (p = 0.852) between groups. However, the proportions of immigrants was significantly higher in the reduced LVEF group compared to the preserved LVEF group (p = 0.011). The percentage of patients with diabetes mellitus (p < 0.001) and CAD (p < 0.001) was significantly lower in the preserved LVEF group compared to the other two groups. In the mildly reduced LVEF group, the percentage of patients with a history of coronary artery bypass graft surgery was significantly higher than in the preserved LVEF group (p = 0.017). The percentages of patients with peripheral arterial disease (p = 0.020) and renal disease (p = 0.018) in the reduced LVEF group were significantly higher than in the preserved LVEF group. The percentage of patients with USAP and NSTEMI in the reduced LVEF group was significantly lower than in the other two groups, while the percentage of patients with STEMI in the preserved LVEF group was significantly lower than in the other two groups (p < 0.001). The median glucose level and mean TyG index in the reduced LVEF group were significantly higher than in the other two groups, and these parameters were also significantly higher in the mildly reduced LVEF group compared to the preserved LVEF group (p < 0.001). The median triglyceride level (p < 0.001) and mortality rate (p < 0.001) in the preserved LVEF group were significantly lower than in the other two groups (Table 1).

Table 1.

Summary of variables with regard to LVEF classification.

		LVEF
	All patients (n = 2135)	Preserved, ≥50% (n = 1351)	Mildly reduced, 41%-49% (n = 399)	Reduced, ≤40% (n = 385)	p
Age,years	57.49 ± 11.45	57.38 ± 11.55	57.97 ± 11.38	57.36 ± 11.16	0.644†
Sex
Female	456 (21.36%)	284 (21.02%)	86 (21.55%)	86 (22.34%)	0.852§
Male	1679 (78.64%)	1067 (78.98%)	313 (78.45%)	299 (77.66%)
Immigrant	131 (6.14%)	67 (4.96%)	31 (7.77%)	33 (8.57%)*	0.011§
Body mass index, kg/m2	28.41 ± 4.00	28.31 ± 3.90	28.35 ± 4.11	28.81 ± 4.18	0.088†
Smoking
Non-smoker	399 (18.88%)	230 (17.22%)	90 (22.84%)	79 (20.63%)	0.063§
Ex-smoker	435 (20.59%)	292 (21.86%)	79 (20.05%)	64 (16.71%)
Passive smoker	161 (7.62%)	108 (8.08%)	27 (6.85%)	26 (6.79%)
Active smoker	1118 (52.91%)	706 (52.84%)	198 (50.25%)	214 (55.87%)
Previous comorbidities
Hypertension	1008 (47.21%)	623 (46.11%)	199 (49.87%)	186 (48.31%)	0.372§
Diabetes mellitus	807 (37.80%)	424 (31.38%)	179 (44.86%)*	204 (52.99%)*	<0.001§
Hyperlipidemia	1430 (66.98%)	886 (65.58%)	275 (68.92%)	269 (69.87%)	0.189§
COPD	142 (6.65%)	94 (6.96%)	28 (7.02%)	20 (5.19%)	0.448§
Coronary artery disease	519 (24.31%)	285 (21.10%)	116 (29.07%)*	118 (30.65%)*	<0.001§
CABG	99 (4.64%)	52 (3.85%)	29 (7.27%)*	18 (4.68%)	0.017§
Peripheral artery disease	77 (3.61%)	40 (2.96%)	14 (3.51%)	23 (5.97%)*	0.020§
Cerebrovascular disease	94 (4.40%)	55 (4.07%)	20 (5.01%)	19 (4.94%)	0.617§
Pulmonary embolism	5 (0.23%)	3 (0.22%)	1 (0.25%)	1 (0.26%)	1.000¶
Deep vein thrombosis	11 (0.52%)	7 (0.52%)	1 (0.25%)	3 (0.78%)	0.503¶
Renal diseases	151 (7.07%)	84 (6.22%)	27 (6.77%)	40 (10.39%)*	0.018§
Malignancy	48 (2.25%)	34 (2.52%)	7 (1.75%)	7 (1.82%)	0.546§
Type of event
USAP	145 (6.79%)	111 (8.22%)	24 (6.02%)	10 (2.60%)*	<0.001§
NSTEMI	836 (39.16%)	567 (41.97%)	144 (36.09%)	125 (32.47%)*
STEMI	1154 (54.05%)	673 (49.81%)	231 (57.89%)*	250 (64.94%)*
Blood glucose, mg/dL	121 (103–158)	115 (100–139)	131 (109–176)*	151 (116–222)*#	<0.001‡
Triglyceride, mg/dL	153 (112–214)	141 (104–194)	171 (129–230)*	178 (126–255)*	<0.001‡
TyG index	4.97 ± 0.31	4.89 ± 0.28	5.05 ± 0.26*	5.16 ± 0.35*#	<0.001†
Mortality	241 (11.29%)	111 (8.22%)	54 (13.53%)*	76 (19.74%)*	<0.001§

Descriptive statistics are presented using mean ± standard deviation for normally distributed continuous variables, median (25th percentile − 75th percentile) for non-normally distributed continuous variables and frequency (percentage) for categorical variables.

^†One-way analysis of variance (ANOVA), ^‡Kruskal Wallis test, ^§Chi-square test, ^¶Fisher-Freeman-Halton test, *Significantly different from “Preserved” group, ^#Significantly different from “Mildly reduced” group. Statistically significant p values are shown in bold.

Abbreviations; CABG: Coronary artery bypass grafting, COPD: Chronic obstructive pulmonary disease, LVEF: Left ventricular ejection fraction, NSTEMI: Non-ST-elevation myocardial infarction, STEMI: ST-elevation myocardial infarction, TyG index: The triglyceride-glucose index, USAP: Unstable angina pectoris.

With a TyG cutoff value of ≥ 4.95, patients with an LVEF below 50 were significantly distinguished with 68.11% sensitivity and 63.21% specificity [AUC (95% CI): 0.698 (0.675–0.721), p < 0.001] (Figure 1). With a cutoff value of ≥ 5.10, patients with an LVEF of 40 or below were significantly distinguished with 57.14% sensitivity and 73.83% specificity [AUC (95% CI): 0.689 (0.658–0.720), p < 0.001] (Figure 2) (Table 2).

Figure 1.

ROC curve of the TyG index to predict mildly reduced or reduced LVEF (<50%).

Figure 2.

ROC curve of TyG index to predict reduced LVEF (≤40%).

Table 2.

Performance of the TyG index to predict LVEF, ROC curve analysis.

	Mildly reduced or Reduced LVEF (<50%)	Reduced LVEF (≤40%)
Cut-off	≥4.95	≥5.10
Sensitivity	68.11%	57.14%
Specificity	63.21%	73.83%
Accuracy	65.01%	70.82%
PPV	51.79%	32.45%
NPV	77.36%	88.68%
AUC (95% CI)	0.698 (0.675–0.721)	0.689 (0.658–0.720)
p	<0.001	<0.001

Statistically significant p values are shown in bold.

Abbreviations; AUC: Area under ROC curve, CI: Confidence interval, LVEF: Left ventricular ejection fraction, NPV: Negative predictive value, PPV: Positive predictive value, ROC: Receiver operating characteristic, TyG index: The triglyceride-glucose index.

There was a significant negative correlation between LVEF and blood glucose level (p < 0.001, r = −0.318), triglyceride level (p < 0.001, r = −0.249), and TyG index (p < 0.001, r = −0.383) (Table 3).

Table 3.

Correlations between LVEF and continuous variables.

	r	p
Age	−0.039	0.068
Body mass index, kg/m2	−0.034	0.116
Blood glucose	−0.318	<0.001
Triglyceride	−0.249	<0.001
TyG index	−0.383	<0.001

r: Spearman correlation coefficient. Statistically significant p values are shown in bold.

Abbreviations; LVEF: Left ventricular ejection fraction, TyG index: The triglyceride-glucose index.

According to multivariable logistic regression analysis results, being an immigrant (OR: 1.754, 95% CI: 1.199–2.566, p = 0.004), having diabetes mellitus (OR: 1.286, 95% CI: 1.046–1.582, p = 0.017), previous CAD (OR: 1.725, 95% CI: 1.359–2.190, p < 0.001), STEMI (OR: 1.847, 95% CI: 1.519–2.247, p < 0.001) and high (≥4.95) TyG index (OR: 3.418, 95% CI: 2.786–4.193, p < 0.001) were independently associated with the likelihood of mildly reduced or reduced LVEF (<50%) (Table 4).

Table 4.

Odds ratios for mildly reduced or reduced LVEF (<50%), logistic regression analysis results.

	Univariable		Multivariable
	OR (95% CI)	p	OR (95% CI)	p
Age	1.002 (0.995–1.010)	0.577
Sex, Male	0.947 (0.765–1.173)	0.618
Immigrant	1.703 (1.195–2.428)	0.003	1.754 (1.199–2.566)	0.004
Body mass index, kg/m2	1.017 (0.995–1.039)	0.135
Smoking, Active smoker	1.007 (0.844–1.202)	0.936
Previous comorbidities
Hypertension	1.128 (0.945–1.345)	0.182
Diabetes mellitus	2.088 (1.742–2.503)	<0.001	1.286 (1.046–1.582)	0.017
Hyperlipidemia	1.190 (0.985–1.437)	0.072
COPD	0.872 (0.609–1.249)	0.456
Coronary artery disease	1.591 (1.301–1.946)	<0.001	1.725 (1.359–2.190)	<0.001
CABG	1.593 (1.063–2.388)	0.024	1.234 (0.768–1.981)	0.385
Peripheral artery disease	1.623 (1.029–2.561)	0.037	1.380 (0.839–2.269)	0.205
Cerebrovascular disease	1.234 (0.810–1.878)	0.327
Pulmonary embolism	1.149 (0.192–6.892)	0.879
Deep vein thrombosis	0.985 (0.287–3.374)	0.980
Renal diseases	1.409 (1.009–1.968)	0.044	1.322 (0.918–1.903)	0.133
Malignancy	0.704 (0.376–1.321)	0.274
Type of event, STEMI	1.599 (1.337–1.913)	<0.001	1.847 (1.519–2.247)	<0.001
TyG index, ≥4.95	3.670 (3.046–4.423)	<0.001	3.418 (2.786–4.193)	<0.001
Nagelkerke R2	–		0.166

Statistically significant p values are shown in bold.

Abbreviations; CABG: Coronary artery bypass grafting, CI: Confidence interval, COPD: Chronic obstructive pulmonary disease, LVEF: Left ventricular ejection fraction, OR: Odds ratio, STEMI: ST-elevation myocardial infarction, TyG index: The triglyceride-glucose index.

When multivariable logistic regression was repeated to detect associations with reduced LVEF (≤40%), independent relationships were identified for being an immigrant (OR: 1.612, 95% CI: 1.044–2.489, p = 0.031), previous CAD (OR: 1.552, 95% CI: 1.188–2.028, p = 0.001), peripheral artery disease (PAD) (OR: 1.783, 95% CI: 1.033–3.079, p = 0.038), renal diseases (OR: 1.709, 95% CI: 1.133–2.578, p = 0.011), STEMI (OR: 2.058, 95% CI: 1.606–2.638, p < 0.001) and high (≥5.10) TyG index (OR: 3.510, 95% CI: 2.716–4.535, p < 0.001) (Table 5).

Table 5.

Odds ratios for reduced LVEF (≤40%), logistic regression analysis results.

	Univariable		Multivariable
	OR (95% CI)	p	OR (95% CI)	p
Age	0.999 (0.989–1.008)	0.802
Sex, Male	0.932 (0.715–1.216)	0.605
Immigrant	1.580 (1.048–2.383)	0.029	1.612 (1.044–2.489)	0.031
Body mass index, kg/m2	1.030 (1.003–1.058)	0.028	1.002 (0.972–1.033)	0.883
Smoking, Active smoker	1.157 (0.926–1.446)	0.199
Previous comorbidities
Hypertension	1.055 (0.846–1.316)	0.633
Diabetes mellitus	2.144 (1.715–2.680)	<0.001	1.222 (0.936–1.596)	0.140
Hyperlipidemia	1.176 (0.926–1.494)	0.183
COPD	0.731 (0.450–1.189)	0.207
Coronary artery disease	1.487 (1.165–1.897)	0.001	1.552 (1.188–2.028)	0.001
CABG	1.011 (0.599–1.705)	0.968
Peripheral artery disease	1.995 (1.209–3.293)	0.007	1.783 (1.033–3.079)	0.038
Cerebrovascular disease	1.159 (0.692–1.942)	0.574
Pulmonary embolism	1.137 (0.127–10.198)	0.909
Deep vein thrombosis	1.710 (0.452–6.476)	0.430
Renal diseases	1.712 (1.171–2.502)	0.006	1.709 (1.133–2.578)	0.011
Malignancy	0.772 (0.344–1.734)	0.531
Type of event, STEMI	1.733 (1.378–2.180)	<0.001	2.058 (1.606–2.638)	<0.001
TyG index, ≥5.10	3.750 (2.985–4.712)	<0.001	3.510 (2.716–4.535)	<0.001
Nagelkerke R2	–		0.139

Statistically significant p values are shown in bold.

Abbreviations; CABG: Coronary artery bypass grafting, CI: Confidence interval, COPD: Chronic obstructive pulmonary disease, LVEF: Left ventricular ejection fraction, OR: Odds ratio, STEMI: ST-elevation myocardial infarction, TyG index: The triglyceride-glucose index.

4. Discussion

It is well established that ACS events have a high propensity to cause myocardial damage and reduced LVEF, which demonstrates weakened cardiac pumping ability and impaired circulation [2]. Reduced LVEF is associated with increased risk of both in-hospital and long-term mortality [7,8,23,24]. Notably, research within subgroups such as STEMI and NSTEMI has demonstrated that low LVEF serves as a strong predictor for major adverse cardiac events (MACE) [7,8,23,24]. Given the importance of LVEF, predicting which patients might experience a decrease in LVEF following ACS and developing targeted strategies for these patients may help reduce ACS-related mortality and morbidity [25,26].

The TyG index was initially proposed as a reliable indicator of insulin resistance and metabolic syndrome, calculated using fasting blood glucose and triglyceride levels [9,10,12]. Subsequently, studies have shown strong associations between the TyG index and cardiovascular disease, its prognosis, as well as HF [10,13,14,16]. The TyG index is a promising surrogate marker of insulin resistance that helps predict cardiovascular risks at various stages of the cardiometabolic continuum [27]. In addition to its cardiometabolic implications, emerging evidence suggests a potential link between the TyG index and depression, particularly in older adults and populations with diabetes or pre-diabetes [28,29]. This study shows that TyG index may be a supportive tool to identify patients at higher risk of HF. A TyG value of ≥ 4.95 significantly predicted patients with < 50% LVEF, while an even higher TyG (≥5.10) was able to predict worse LVEF (≤40%). This directional relationship was further supported by the moderate correlation between TyG and LVEF values. Furthermore, we determined that having higher TyG indices (of ≥ 4.95 and ≥ 5.10) were independently associated with LVEF values of < 50% and ≤ 40%, respectively.

The literature contains numerous studies reporting a significant association between the TyG index and HF. Higher TyG index was significantly associated with an increased risk of HF, and the results also showed that each unit of increase in TyG index was associated with a 15% higher risk of HF [30]. A meta-analysis also found that a higher TyG index was significantly associated with an increased risk of all-cause mortality, cardiovascular deaths, HF-related hospital readmissions, and MACE [31]. Lyu et al. linked the TyG-BMI index with all-cause mortality and hospitalizations in HF patients [13]. Many similar studies have suggested a prognostic association between the TyG index and HF [14,15].

Although the relationship between the TyG index and the prognosis of ACS patients has been widely investigated, studies focusing specifically on the association between TyG and post-ACS LVEF or HF are relatively limited, which prevents its use as an early indicator in the clinical setting. Considering that higher TyG index has been associated with LVEF, and elevated levels independently increase the risks for HF and MACE [11], it is likely that TyG has potential as an early marker of myocardial injury. This link is supported by a study on patients with STEMI, in which the authors reported a relationship between LVEF and higher TyG-BMI index [32]. High TyG has also been associated with MACE risk and other adverse cardiovascular outcomes [18,19], particularly in subjects with < 50% LVEF following myocardial infarction, while such a relationship was absent in patients with normal LVEF [9]. Results similar to ours have been described in other studies. Guo et al. observed lower LVEF in patients with a higher TyG index (32), and Huang et al. found a higher prevalence of LVEF ≤ 40% among patients with a high TyG index [14]. Conversely, in a multicenter retrospective cohort study of STEMI patients undergoing percutaneous coronary intervention, no statistically significant association was found between LVEF and the TyG index [20]. Similarly, another study on STEMI patients found no significant relationship between TyG at admission and LVEF measured within 3 days after percutaneous intervention [21]. Chen et al. explored the link between the TyG index and early-onset HF in STEMI patients undergoing PCI, and described a higher incidence of early-onset HF in females, but no association with TyG or TyG-BMI index after adjustment [33]. Although studies focusing on percutaneous interventions have not found considerable relationships, and interesting study examining re-stenosis showed that high TyG index could increase re-stenosis risks following drug-eluting percutaneous intervention [1]. In Gao et al.’s study, LVEF did not differ significantly across TyG index tertiles in patients with myocardial infarction and non-obstructive coronary arteries, although higher TyG levels were associated with a higher incidence of MACE [34]. Zhang et al. also found no significant association between baseline TyG index and LVEF in patients with myocardial infarction, but a high TyG index was independently associated with all-cause mortality, cardiac death, revascularization, cardiac rehospitalization, and MACE [35]. Cheng et al. [36] and Sun et al. [37] similarly reported no significant LVEF differences between high and low TyG index groups.

Most studies have focused on the relationship between the TyG index and ACS prognosis. In a meta-analysis, a significant association was found between higher TyG index levels and increased risk of cardiovascular disease, despite lack of relationships with cardiovascular or all-cause mortality [4]. Another meta-analysis supported prior data in showing relationships between MACE development and TyG values [16]. Guo et al. observed a significant relationship between low LVEF (<40%) during admission for myocardial infarction and triglyceride levels [12]. In another study, high TyG index (≥9.432) was identified as an independent risk factor for CAD in postmenopausal women – even after adjusting for LVEF (<50%) [38]. A retrospective study found a significant relationship between high TyG index and myocardial ischemia risk, evaluated using fractional flow reserve by computed tomography in patients with minimal to moderate CAD [10]. Additionally, many recent studies have reported a significant association between TyG levels and the onset, progression, and prognosis of ACS [11,20–22,32,39,40].

In our study, although no independent relationship was found between glucose levels or triglyceride levels alone and low LVEF, the detection of a weak negative correlation between TyG and LVEF is notable. The results suggest that the TyG index may have prognostic value in ACS and HF patients. However, the conflicting findings in studies examining the relationship between TyG and LVEF, the weak correlation observed in this study, and the low-to-moderate sensitivity and specificity values indicate the need for further research to better predict LVEF decline after ACS using TyG or improved markers.

The secondary aim of the study was to explore other factors, aside from TyG, that could predict low LVEF after ACS. In addition to high TyG, factors such as being an immigrant, having a history of CAD, PAD, renal disease, diabetes, and STEMI were found to be associated with lower LVEF post-ACS. These findings are largely consistent with results from previous studies. Various studies have shown that factors like high pulmonary arterial pressure, elevated cardiac enzyme levels, female sex, older age, diabetes, hypertension, a history of angina, and CAD are strongly linked to decreased LVEF and HF [41,42]. Both CAD and PAD increase the risk of myocardial damage due to restricted blood flow, leading to ischemia and infarction. This compromised blood supply to the heart can directly reduce LVEF by impairing myocardial contractility and function [43]. Chronic kidney disease is associated with poor cardiovascular outcomes, including decreased LVEF. Renal dysfunction exacerbates fluid retention, hypertension, and increases the risk of HF, all of which negatively affect LVEF [44]. Diabetes accelerates atherosclerosis and impairs myocardial function due to metabolic disturbances, contributing to higher rates of HF and lower LVEF through both microvascular and macrovascular complications [45]. Older age is generally associated with a decline in LVEF due to age-related changes in cardiac structure and function [46]. In women, hormonal changes and a higher incidence of comorbidities, such as diabetes, can further increase the risk of LVEF reduction [40]. Immigrants may face heightened health risks due to factors such as limited healthcare access, socioeconomic challenges, and potentially different lifestyle and dietary habits compared to the general population [47].

Despite the large scale of the present study, data were collected from a single center, which limits generalizability. The retrospective data collection also limited the analyses to readily-available variables. Despite the availability of complete data for the majority of cases, some key information could not be reliably or comprehensively collected for a significant number of patients, resulting in their exclusion from the study. For example, detailed descriptions of treatment procedures for ACS, which may influence LVEF outcomes, were not included. Only glucose and triglyceride levels were analyzed, excluding other laboratory values and cardiac markers that might serve as confounding factors. Another critical limitation is the absence of LVEF measurements before or at the time of ACS diagnosis, which prevents longitudinal assessment of the amount of change. Additionally, the TyG index could be confounded by undiagnosed diabetes or hyperlipidemia.

5. Conclusion

Higher TyG index values were independently associated with low LVEF – as measured one week after ACS. However, the sensitivity and specificity of TyG for predicting low LVEF were relatively low. In addition to TyG index, factors such as immigrant status, CAD, PAD, renal disease, diabetes, and STEMI were identified as independent risk factors for low LVEF, which have considerable clinical implications. Our findings suggest that TyG index may be an important marker for identifying patients at high risk of HF after ACS. However, further research is needed to validate these and compare LVEF values measured at the time of diagnosis with subsequent LVEF measurements.

Funding Statement

No funding received.

Article highlights

What We Studied:

• We explored whether the triglyceride-glucose (TyG) index, a simple measure of metabolic health, could help predict how well the heart functions after an acute coronary syndrome (ACS) event.

What We Found:

• Patients with a higher TyG index were more likely to have reduced left ventricular ejection fraction (LVEF), which indicates weaker heart function.

• A TyG index of 4.95 or higher was linked to a greater chance of having LVEF below 50%, with moderate accuracy in distinguishing those at risk.

• A TyG index of 5.10 or higher was even more strongly associated with severely reduced heart function (LVEF ≤40%).

• Other key factors tied to lower LVEF included immigrant status, diabetes, previous coronary artery disease (CAD), peripheral artery disease (PAD), kidney disease, and having a STEMI-type heart attack.

Why It Matters:

• The TyG index is a simple, affordable tool that could help doctors identify patients at higher risk for heart failure after ACS.

• While promising, it should be used alongside other well-established clinical markers rather than on its own.

• Understanding metabolic health through markers like TyG could open new doors for early intervention and better long-term heart care.

What’s Next:

• More research is needed to confirm how well the TyG index predicts heart health in different patient groups and whether lifestyle or medical interventions targeting high TyG levels could improve outcomes after a heart attack.

Author contribution

OO and EB designed this study. OO collected the data and drafted the manuscript. EB analyzed the data. OO and EB revised the manuscript. All authors contributed to the article and approved the submitted version.

Disclosure statement

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate institutional review board approval (institutional ethics committee of Haseki Education and Research Hospital, Istanbul, Turkey (Decision date: 23 May 2024, decision no: 14–2024).) and/or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.