Abstract
Background
Emerging evidence has highlighted the connection between the Triglyceride-glucose (TyG) index and the development and severity of coronary artery disease. However, the role of the TyG index in predicting adverse cardiovascular outcomes among patients who have undergone percutaneous coronary intervention (PCI) remains underexplored.
Methods
Our study encompassed 8019 individuals with acute coronary syndrome (ACS) who had PCI, sourced from the ongoing perspective, observational, single-center COSTIC research. We gathered data on baseline clinical characteristics and the TyG index. The primary endpoints were major adverse cardiovascular events (MACE), which included cardiovascular death, all-cause death, myocardial infarction, and stroke. To examine the relationship between the TyG index and cardiovascular outcomes, we utilized multivariate Cox proportional hazards models and restricted cubic splines.
Results
During the 1-year follow-up period, we documented 341 MACEs, comprising 197 cardiovascular deaths and 242 all-cause deaths. The TyG index was associated with a higher risk of MACE (hazard ratio [HR]: 1.246, 95% confidence interval [CI] 1.064 to 1.459, p = 0.006), cardiovascular death (HR: 1.409, 95% CI 1.150 to 1.727, p = 0.001) and all-cause death (HR: 1.368, 95% CI 1.133 to 1.652, p = 0.001) for each interquartile range (IQR) increment. Stratified analyses unveiled significant interactions between the TyG index and variables such as sex, smoking status, and ACS diagnosis (p for interaction < 0.05). Nevertheless, restricted cubic splines analysis did not detect a non-linear relationship between the TyG index and MACE (p-non-linear = 0.971), cardiovascular mortality (p-non-linear = 0.684), or all-cause mortality (p-non-linear = 0.827).
Conclusions
Our findings underscore a significant association between the TyG index and adverse cardiovascular outcomes, including cardiovascular and all-cause mortality, in ACS patients following PCI. The TyG index may thus function as an early predictor of cardiovascular risk or a potential therapeutic target in the management of cardiovascular disease.
Graphical abstract
Triglyceride-glucose (TyG) index predicts cardiovascular events in patients with acute coronary syndrome undergoing percutaneous coronary intervention.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12933-025-02937-9.
Keywords: The TyG index, Percutaneous coronary intervention, Acute coronary syndrome, Mortality, Prognosis, Cohort study
Research insights
What is currently known about this topic?
The TyG index, reflecting insulin resistance, is associated with increased risk of cardiovascular disease (CVD). Studies have shown that a high TyG index predicts increased prevalence and incidence of atherosclerosis, coronary artery disease, stroke, etc., independent of traditional risk factors.
What is the key research question?
Does the TyG index predict major adverse cardiovascular events (MACE), particularly cardiovascular and all-cause mortality, in ACS patients after PCI? Specifically, does the association between the TyG index and adverse cardiovascular events follow a linear or non-linear pattern?
What is new?
TyG index has a significant, linear association with MACE in ACS patients who underwent PCI in a large, prospective cohort.
The association between TyG index and risk of cardiovascular events is independent of traditional risk factors.
Stratified analyses demonstrated significant interaction between TyG index and sex, smoking and diagnosis of ACS (p for interaction < 0.05).
How might this study influence clinical practice?
Triglyceride-glucose (TyG) index may serve as an early indicator for risk prediction of cardiovascular disease particularly in those high risk population. Treatment targeting TyG index may further improve prognosis of cardiovascular disease.
Introduction
Despite substantial advancements in treatment modalities, cardiovascular disease (CVD) continues to be a leading cause of mortality worldwide [1]. Additionally, patients with CVD have a median 10-year incidence rate of major adverse cardiovascular events (MACE) of 17% [2]. Early prediction of MACE and precise risk stratification among patients with CVD may further improve disease prognosis.
Insulin resistance (IR) is a common metabolic disorder that is strongly linked to an increased risk of CVD [3–5]. However, the clinical application of IR assessment is limited by the complexity of the hyperinsulinaemic - euglycaemic clamp, which is considered the gold standard for evaluating IR. The triglyceride-glucose (TyG) index serves as an early and robust marker of insulin resistance [6]. A higher TyG index has been associated with decreased insulin sensitivity and an increased risk of type 2 diabetes [7–11]. Recent studies have demonstrated that the TyG index is not only a marker of IR but also a predictor of various adverse health outcomes. It has been shown to be associated with an increased risk of cardiovascular diseases, including atherosclerotic cardiovascular disease [12–16], myocardial infarction [17], as well as other non-communicable diseases, including chronic kidney disease [18, 19], cancer [20, 21], and dementia [22]. Despite these findings, the relationship between the TyG index and cardiovascular events, particularly cardiovascular mortality in patients with acute coronary syndrome (ACS), remains underexplored.
Although several studies have demonstrated that the TyG index is associated with an increased risk of MACE or adverse outcomes in ACS patients [23–29], including those undergoing PCI, these studies have been limited by small sample sizes, insufficient follow-up periods, or focused on specific subgroups such as diabetic patients, which restrict the generalizability of their conclusions. Only a limited number of prospective cohort study examined the predictive value of the TyG index for long-term mortality in ACS patients, with one small study indicating that the TyG index is an independent predictor of all-cause mortality in elderly ACS patients [30]. Therefore, large-scale prospective studies are still warranted to assess the relationship between the TyG index and cardiovascular and all-cause mortality in a broader ACS population, especially those undergoing PCI. Such studies could provide valuable insights into whether the TyG index could serve as a viable intervention target to enhance clinical outcomes and improve prognosis in this high-risk patient population. Moreover, the nature of the relationship between the TyG index and cardiovascular mortality is not fully understood. It is unclear whether this relationship follows a U-shaped curve, a pattern similar to that observed between low-density lipoprotein cholesterol (LDL-C) levels and all-cause mortality [31]. Further large-scale, well-designed studies are warranted to elucidate this association between the TyG index and cardiovascular mortality in ACS patients. In general, prospectively evaluating the association between TyG index and cardiovascular events could uncover potential mechanistic insights into the interplay between glycolipid metabolic pathways and atherosclerotic cardiovascular disease outcomes [32].
In the present study, we leveraged our cohort to investigate the impact of the TyG index on cardiovascular events, particularly cardiovascular and all-cause mortality, in patients with acute coronary syndrome who underwent percutaneous coronary intervention (PCI). Additionally, we tested for potential non-linear associations in this context.
Methods
Study design and eligibility
The procedure of sample recruitment, inclusion criteria, data collection and definition of risk factors have been previously described [33]. The 1-year follow-up rate of the study cohort was 98.5%. Baseline characteristics of the study participants are summarized in Table 1. In brief, a total of 9039 ACS patients who underwent PCI at Tongji Hospital (Wuhan, China) were consecutively enrolled between August 2014 and July 2020. The diagnosis of ACS was based on clinical presentation, electrocardiogram (ECG) abnormalities, and biomarker test, according to the AHA/ACC Guidelines [34, 35]. After excluding 1020 cases (11.3%) with missing TyG index records at baseline, a final cohort of 8019 participants was included in the analysis. This study was conducted in accordance with the Declaration of Helsinki. Approval was obtained from the institutional ethics committee of the local hospital (TJC20160202), and written informed consent was obtained from all participants.
Table 1.
Clinical characteristics of the study participants
| Characteristics | Quartiles of TyG index | P value | ||||
|---|---|---|---|---|---|---|
| overall | Q1 (6.22–8.48) | Q2 (8.48–8.96) | Q3 (8.96–9.48) | Q4 (9.48–13.77) | ||
| N (%) | 8019 | 2005 (25.0) | 2005 (25.0) | 2005 (25.0) | 2004 (25.0) | |
| Age, years | 60.7 (10.5) | 62.2 (10.5) | 61.3 (10.6) | 60.2 (10.5) | 59.1 (10.1) | < 0.001 |
| Male, n (%) | 5847 (72.91) | 1533 (76.46) | 1450 (72.32) | 1461 (72.87) | 1403 (70.01) | < 0.001 |
| Risk factors | ||||||
| Smoking | 3400 (42.40) | 911 (45.44) | 811 (40.45) | 863 (43.04) | 815 (40.67) | 0.004 |
| Hypertension | 4887 (60.94) | 1090 (54.36) | 1198 (59.75) | 1290 (64.34) | 1309 (65.32) | < 0.001 |
| Diabetes | 2444 (30.48) | 251 (12.52) | 404 (20.15) | 678 (33.82) | 1111 (55.44) | < 0.001 |
| Dyslipidemia | 3777 (47.10) | 460 (22.94) | 731 (36.46) | 1111 (55.41) | 1475 (73.60) | < 0.001 |
| Medical history | ||||||
| Previous MI or CABG | 790 (9.85) | 184 (9.18) | 197 (9.83) | 208 (10.37) | 201 (10.03) | 0.633 |
| Previous stroke | 614 (7.66) | 170 (8.48) | 154 (7.68) | 158 (7.88) | 132 (6.59) | 0.151 |
| Previous PAD | 1625 (20.26) | 373 (18.60) | 445 (22.20) | 394 (19.65) | 413 (20.61) | 0.034 |
| Arrhythmia | 840 (10.48) | 242 (12.07) | 213 (10.62) | 214 (10.67) | 171 (8.53) | 0.003 |
| COPD | 206 (2.57) | 69 (3.44) | 50 (2.49) | 49 (2.44) | 38 (1.90) | 0.020 |
| Chronic kidney disease | 665 (8.29) | 144 (7.18) | 126 (6.28) | 177 (8.83) | 218 (10.88) | < 0.001 |
| Prior bleeding | 126 (1.57) | 36 (1.80) | 34 (1.70) | 25 (1.25) | 31 (1.55) | 0.527 |
| Chronic gastritis | 534 (6.66) | 128 (6.38) | 137 (6.83) | 141 (7.03) | 128 (6.39) | 0.794 |
| Peptic ulcer | 166 (2.07) | 53 (2.64) | 40 (2.00) | 39 (1.95) | 34 (1.70) | 0.184 |
| Pre-hospital statin use | 1872 (23.34) | 421 (21.00) | 448 (22.34) | 466 (23.24) | 537 (26.80) | < 0.001 |
| TyG index | 8.96 (8.48, 9.48) | 8.18 (7.95, 8.34) | 8.74 (8.61, 8.85) | 9.20 (9.08, 9.34) | 9.88 (9.65, 10.2) | < 0.001 |
| TC, mmol/L | 4.07 (1.15) | 3.74 (1.02) | 3.99 (1.08) | 4.12 (1.10) | 4.45 (1.26) | < 0.001 |
| TG, mmol/L | 1.83 (1.53) | 0.78 (0.30) | 1.28 (0.37) | 1.84 (0.56) | 3.43 (2.21) | < 0.001 |
| HDL-C, mmol/L | 1.01 (0.26) | 1.10 (0.28) | 1.03 (0.26) | 0.98 (0.24) | 0.92 (0.22) | < 0.001 |
| LDL-C, mmol/L | 2.53 (0.97) | 2.41 (0.95) | 2.57 (0.97) | 2.60 (0.97) | 2.53 (0.97) | < 0.001 |
| Admission glucose levels, mmol/L | 7.54 (3.45) | 5.83 (1.39) | 6.47 (1.84) | 7.48 (2.61) | 10.40 (4.82) | < 0.001 |
| HbA1c, % | 6.36 (1.30) | 5.80 (0.64) | 6.02 (0.89) | 6.35 (1.16) | 7.18 (1.73) | < 0.001 |
| Diabetes complications | 703 (8.77) | 70 (3.49) | 126 (6.28) | 189 (9.43) | 318 (15.9) | < 0.001 |
| hs-CRP* | < 0.001 | |||||
| < 1 mg/L | 1611 (26.03) | 458 (29.74) | 415 (27.34) | 375 (24.27) | 363 (22.87) | |
| [1,3] mg/L | 1921 (31.03) | 422 (27.40) | 461 (30.37) | 485 (31.39) | 553 (34.85) | |
| (3,10] mg/L | 1447 (23.38) | 342 (22.21) | 301 (19.83) | 387 (25.05) | 417 (26.28) | |
| > 10 mg/L | 1211 (19.56) | 318 (20.65) | 341 (22.46) | 298 (19.29) | 254 (16.01) | |
| Post-PCI LDL-C, mmol/L | 2.37 (0.96) | 2.28 (0.93) | 2.41 (0.97) | 2.42 (0.97) | 2.37 (0.97) | < 0.001 |
| BMI, kg/m^2 | 24.7 (3.3) | 23.4 (3.3) | 24.4 (3.2) | 25.0 (3.2) | 25.7 (3.1) | < 0.001 |
| Diagnosis of ACS | < 0.001 | |||||
| ST-elevation MI | 1734 (21.62) | 591 (29.48) | 432 (21.55) | 388 (19.35) | 323 (16.12) | |
| Non–ST-elevation MI | 1706 (21.27) | 414 (20.65) | 438 (21.85) | 438 (21.85) | 416 (20.76) | |
| Unstable angina | 4579 (57.10) | 1000 (49.88) | 1135 (56.61) | 1179 (58.80) | 1265 (63.12) | |
| Killip class > 2 | 718 (8.95) | 166 (8.28) | 184 (9.18) | 190 (9.48) | 178 (8.88) | 0.588 |
| Thrombolytic therapy before PCI | 168 (2.10) | 56 (2.79) | 34 (1.70) | 45 (2.24) | 33 (1.65) | 0.037 |
| Artery access | 0.634 | |||||
| Radial or ulnar artery | 7482 (93.30) | 1864 (92.97) | 1871 (93.32) | 1866 (93.07) | 1881 (93.86) | |
| Brachial artery | 275 (3.43) | 75 (3.74) | 67 (3.34) | 77 (3.84) | 56 (2.79) | |
| Femoral artery | 262 (3.27) | 66 (3.29) | 67 (3.34) | 62 (3.09) | 67 (3.34) | |
| Three vessels disease | 1035 (12.91) | 195 (9.73) | 217 (10.82) | 287 (14.31) | 336 (16.77) | < 0.001 |
| Gensini score | 52 (31, 82) | 50 (30, 80) | 50 (30, 80) | 52 (32, 84) | 54 (32, 84) | 0.001 |
| Number of stents | 2 (1, 2) | 1 (1, 2) | 2 (1, 2) | 2 (1, 2) | 2 (1, 3) | < 0.001 |
| Total length of stents | 38 (24, 65) | 36 (24, 60) | 36 (24, 63) | 42 (26, 66) | 44 (28, 69) | < 0.001 |
| TIMI flow after PCI | 0.017 | |||||
| TIMI 0–1 | 9 (0.11) | 1 (0.05) | 6 (0.30) | 1 (0.05) | 1 (0.05) | |
| TIMI 2 | 65 (0.81) | 25 (1.25) | 16 (0.80) | 11 (0.55) | 13 (0.65) | |
| TIMI 3 | 7945 (99.08) | 1979 (98.70) | 1983 (98.90) | 1993 (99.40) | 1990 (99.30) | |
| Medications | ||||||
| Aspirin | 7924 (98.82) | 1983 (98.90) | 1982 (98.85) | 1984 (98.95) | 1975 (98.55) | 0.647 |
| Ticagrelor | 4331 (54.01) | 1052 (52.47) | 1064 (53.07) | 1088 (54.26) | 1127 (56.24) | 0.082 |
| Statins | 7923 (98.80) | 1986 (99.05) | 1983 (98.90) | 1977 (98.60) | 1977 (98.65) | 0.516 |
| ACE inhibitors | 6162 (76.84) | 1517 (75.66) | 1532 (76.41) | 1547 (77.16) | 1566 (78.14) | 0.283 |
| Beta-blockers | 6237 (77.78) | 1479 (73.77) | 1560 (77.81) | 1577 (78.65) | 1621 (80.89) | < 0.001 |
| Nitrates | 1705 (21.26) | 431 (21.50) | 436 (21.75) | 408 (20.35) | 430 (21.46) | 0.708 |
| Diuretics | 998 (12.45) | 223 (11.12) | 250 (12.47) | 241 (12.02) | 284 (14.17) | 0.029 |
Values are mean (SD) or n (%). MI, myocardial infarction; CABG, coronary artery bypass graft; PAD, peripheral artery disease; COPD, chronic obstructive pulmonary disease; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; HbA1c, glycated hemoglobin; hs-CRP, high-sensitivity C-reactive protein; BMI, body mass index; ACS, acute coronary artery syndrome; PCI, percutaneous coronary intervention; TIMI, thrombolysis in myocardial infarction; ACE, angiotensin-converting enzyme. * With 1829 missing values.
Baseline clinical characteristics
All baseline clinical characteristics were extracted from the electronic medical record, including demographic characteristics (age, sex), risk factors (smoking, hypertension, diabetes, and dyslipidemia), self-reported medical history (myocardial infarction [MI], coronary artery bypass grafting [CABG], stroke, peripheral artery disease [PAD], arrhythmia, chronic obstructive pulmonary disease [COPD], chronic kidney disease [CKD], bleeding, chronic gastritis, and peptic ulcer), laboratory test results (glucose, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), glycated hemoglobin (HbA1c) and high-sensitivity C-reactive protein (hs-CRP)), ACS characteristics (classification of ACS, Killip classes), coronary angiography and PCI details (thrombolytic therapy before PCI, artery access, three vessels disease, Gensini score, number of stents, total length of stents, and TIMI flow after PCI), and medication use (Aspirin, Ticagrelor, Statins, ACE inhibitors, Beta-blockers, Nitrates, and Diuretics). Pre-hospital statin use, diabetes complications and BMI were also recorded. Fasting plasma glucose and triglyceride levels, which are used to calculate the TyG index, were measured after an overnight fast according to the local hospital’s established clinical laboratory protocols. The TyG index was calculated using ln[fasting blood TG (mg/dl) × fasting blood glucose (mg/dl)/2] [6]. Dyslipidemia was defined according to the following criteria: total cholesterol (TC) ≥ 5.18 mmol/L (200 mg/dL), low-density lipoprotein cholesterol (LDL-C) ≥ 3.37 mmol/L (130 mg/dL), and/or triglycerides (TG) ≥ 1.7 mmol/L (150 mg/dL), with or without high-density lipoprotein cholesterol (HDL-C) < 1.04 mmol/L (40 mg/dL). ACS was stratified as ST-elevation MI, Non–ST-elevation MI and unstable angina. The coronary angiography and PCI details were provided in our previous report [33].
End points and definitions
The primary outcomes included cardiovascular death, all-cause death, MI and stroke, collectively referred to as MACE. Mortality data were meticulously collected at multiple follow-up intervals, specifically at 7 days, 1 month, 6 months, and 1-year post-enrollment. These data were gathered through face-to-face interviews, telephone calls, and/or comprehensive chart reviews, adhering to the methodologies previously detailed [33]. Cardiovascular death was defined as mortality resulting from significant cardiovascular or cerebrovascular diseases. All-cause death, conversely, encompassed fatalities from any etiology, without restriction to specific disease categories. The diagnosis of MI adhered to the criteria stipulated in the Fourth Universal Definition of Myocardial Infarction [36]. Stroke was characterized by a rapid onset of neurological deficits attributable to an ischemic event within the central nervous system, corroborated by imaging studies. For a diagnosis of stroke to be confirmed, the neurological deficits had to persist for a minimum duration of 24 h from the time of symptom onset.
Statistical analysis
The statistical analyses were conducted using R software version 4.4.1 (The R Foundation; http://www.R-project.org). Continuous variables were assessed for normality using the Shapiro-Wilk test and visual inspection of Q-Q plots and were described as mean ± standard deviation (SD) or median (interquartile range, IQR), while categorical variable were characterized as number (percentage). Based on their distribution, differences in continuous variables between groups were assessed using one-way ANOVA or the Kruskal-Wallis test, as appropriate. Differences in categorical variables were assessed using the chi-squared test. Multivariate Cox proportional hazards regression models were used to evaluate the association between the TyG index and the risk of MACE, CVD mortality and all-cause mortality. We handled missing data using multiple imputation by chained equations (MICE) and fitted Cox proportional hazards models to each of the imputed datasets. Results were pooled using Rubin’s rules, and all analyses were conducted in R with the mice and survival packages. To account for potential confounding factors, three different models were used. Model 1 was unadjusted, Model 2 was adjusted for age and gender, and Model 3 was adjusted for age, gender, smoking, hypertension, previous MI or CABG, stroke, arrhythmia, COPD, chronic kidney disease, laboratory findings (TC, TG, HDL-C, LDL-C, admission glucose levels, HbA1c, post-PCI LDL-C), diabetes complications, BMI, pre-hospital statin use, diagnosis of ACS, Killip class, artery access, number of stents, TIMI flow after PCI and medications (aspirin, statins, ACE inhibitors, beta blockers). For the analysis of non-linear associations, we utilized Cox proportional hazards regression models with restricted cubic splines and smooth curve fitting (penalized spline method) through R packages rcssci. We also tested for nonlinear relationships by including both linear and quadratic terms of the mean-centered TyG index in Cox regression models. Stratified analyses were performed based on age (≥ 60 years old or < 60 years old), gender, smoking, hypertension, diabetes and diagnosis of ACS. P value less than 0.05 was considered statistically significant.
Results
Clinical characteristics of the study participants
In this study, a total of 9039 ACS patients who underwent PCI were continuously enrolled between August 2014 and July 2020. After excluding cases with missing TyG index values, 8019 participants were included in the final analysis (Table 1). The mean age of the study population was 60.7 ± 10.5 years, with the majority being males (72.91%). Higher levels of the TyG index were associated with increased incidences of hypertension, diabetes and dyslipidemia. The occurrence of PAD and CKD were higher while arrhythmia and COPD were lower in patients with elevated TyG index at baseline. Plasma levels of hs-CRP were also increased in higher TyG index group. Not surprisingly, admission glucose levels, HbA1c, and BMI, along with the prevalence of diabetes complications and prehospital statin use, were significantly elevated or more common in the higher TyG index group. Strikingly, the proportions of ST-elevation myocardial infarction and thrombolytic therapy before PCI were more common in lower TyG index group. The coronary artery intervention procedure revealed that a higher TyG index was associated with more frequent three-vessels disease, higher Gensini score, more stents implantation and longer total length of stents. Of note, post-PCI TIMI flow was slightly better in the higher TyG index group compared to the lower TyG index group. Regarding medications during follow-up, beta blocker and diuretics were more frequently used in the higher TyG index group.
Association between the TyG index and MACE and cardiovascular or all-cause mortality
During the 1-year follow-up period, a total of 341 MACEs were documented, which included 197 cardiovascular deaths and 242 all-cause deaths (Table S1). Kaplan-Meier analysis was performed to assess the association between the TyG index and the risk of adverse cardiovascular events (Figure S1). The TyG index was significantly associated with an increased risk of MACE after adjusting for traditional risk factors and comorbidities (HR: 1.246, 95% confidence interval [CI] 1.064 to 1.459, p = 0.006, per IQR change, Table 2). For CVD mortality, the TyG index exhibited a more pronounced and statistically significant association (HR: 1.409, 95% CI: 1.150 to 1.727, p = 0.001, per IQR change). The linkage between TyG index and all-cause mortality was also revealed in our study (HR: 1.368, 95% CI: 1.133 to 1.652, p = 0.001, per IQR change).
Table 2.
HRs (95% CIs) for cardiovascular events according to the TyG index quartiles
| Quartiles of TyG index | |||||
|---|---|---|---|---|---|
| Q1 (6.22–8.48) | Q2 (8.48–8.96) | Q3 (8.96–9.48) | Q4 (9.48–13.77) | P trend | |
| MACE | |||||
| Number of MACEs | 88 | 72 | 92 | 89 | |
| Model 1 | 0.561 | ||||
| HR (95%CI) P-value | 1 | 0.814 (0.596, 1.112) 0.312 | 1.048 (0.783, 1.404) 0.314 | 1.013 (0.754, 1.360) 0.228 | 1.029 (0.935, 1.131) |
| Model 2 | 0.083 | ||||
| HR (95%CI) P-value | 1 | 0.851 (0.623, 1.163) 0.312 | 1.163 (0.867, 1.560) 0.314 | 1.202 (0.891, 1.620) 0.228 | 1.089 (0.989, 1.200) |
| Model 3 | 0.006 | ||||
| HR (95%CI) P-value | 1 | 1.015 (0.727, 1.417) 0.930 | 1.423 (0.992, 2.042) 0.056 | 1.941 (1.191, 3.163) 0.008 | 1.246 (1.064, 1.459) |
| CVD mortality | |||||
| Number of deaths | 47 | 35 | 58 | 57 | |
| Model 1 | 0.088 | ||||
| HR (95%CI) P-value | 1 | 0.742 (0.479, 1.150) 0.182 | 1.238 (0.843, 1.819) 0.276 | 1.215 (0.826, 1.788) 0.323 | 1.115 (0.984, 1.265) |
| Model 2 | 0.011 | ||||
| HR (95%CI) P-value | 1 | 0.771 (0.497, 1.194) 0.244 | 1.366 (0.928, 2.011) 0.114 | 1.433 (0.969, 2.119) 0.072 | 1.180 (1.039, 1.341) |
| Model 3 | 0.001 | ||||
| HR (95%CI) P-value | 1 | 0.978 (0.612, 1.562) 0.926 | 1.814 (1.131, 2.908) 0.014 | 2.610 (1.401, 4.864) 0.003 | 1.409 (1.150, 1.727) |
| All-cause mortality | |||||
| Number of deaths | 63 | 44 | 72 | 63 | |
| Model 1 | 0.408 | ||||
| HR (95%CI) P-value | 1 | 0.696 (0.474, 1.023) 0.065 | 1.147 (0.818, 1.608) 0.427 | 1.002 (0.707, 1.421) 0.992 | 1.049 (0.937, 1.174) |
| Model 2 | 0.064 | ||||
| HR (95%CI) P-value | 1 | 0.728 (0.495, 1.071) 0.107 | 1.279 (0.910, 1.797) 0.156 | 1.200 (0.842, 1.709) 0.314 | 1.114 (0.994, 1.250) |
| Model 3 | 0.001 | ||||
| HR (95%CI) P-value | 1 | 0.938 (0.620, 1.419) 0.761 | 1.761 (1.148, 2.701) 0.009 | 2.368 (1.325, 4.231) 0.004 | 1.368 (1.133, 1.652) |
Model 1: Non-adjusted
Model 2: Adjusted for age and gender
Model 3: Adjusted for age, gender, smoking, hypentension, previous MI or CABG, previous stroke, arrhythmia, COPD, chronic kidney disease, TC, TG, HDL-C, LDL-C, admission glucose levels, HbA1c, diabetes complications, post-PCI LDL-C, BMI, Killip class, pre-hospital statin use, aspirin, statins, ACE inhibitors, beta blockers, diagnosis of ACS, artery access, number of stents and TIMI flow after PCI. HR: Hazard ratio; CI: Confidence interval
Results from analyses at different follow-up time points also showed that the TyG index was associated with an elevated risk of MACE, cardiovascular death, and all-cause mortality at 1 and 6 months, but not at 7 days (Table S2-4). The results of the sensitivity analysis excluding outcomes within the first 7 days (performed to mitigate potential confounding by stress hyperglycemia) were consistent with the main analysis (Table S5).
Non-linear association analysis between TyG index and cardiovascular events
In this study, non-linear association analysis was also performed to explore the relationship between the TyG index and cardiovascular events in ACS patients who underwent PCI. However, no statistically significant non-linear associations were found between the TyG index and MACE (p = 0.971), CVD mortality (p = 0.684), or all-cause mortality (p = 0.827) (Fig. 1A–C). The results were also consistent when we tested for non-linearity by including a quadratic term for the centered TyG index in the Cox regression model (Table S6). As shown in Fig. 1, a higher TyG index was associated with a significantly increased risk of adverse cardiovascular events, especially in patients with values above 10.
Fig. 1.
Association between TyG index and MACE (A), CVD mortality (B) and all-cause mortality (C) in ACS patients who underwent PCI. Each hazard ratio was computed with a TyG index level of A 10.376, B 10.583 and C 10.322 as the reference. Adjusted for age, gender, smoking, hypentension, previous MI or CABG, previous stroke, arrhythmia, COPD, chronic kidney disease, TC, TG, HDL-C, LDL-C, admission glucose levels, HbA1c, diabetes complications, post-PCI LDL-C, BMI, Killip class, pre-hospital statin use, aspirin, statins, ACE inhibitors, beta blockers, diagnosis of ACS, artery access, number of stents and TIMI flow after PCI. The solid line and red area represent the estimated values and their corresponding 95% CIs, respectively (TyG index: triglyceride-glucose index)
Subgroup assessments
Subgroup analysis was conducted based on age, gender, smoking status, hypertension, diabetes and diagnosis of ACS. The incidence rate of MACE was significantly higher in female, smokers and patients with ST-elevation myocardial infarction group as the increase of TyG index (p for interaction = 0.011, 0.043, and 0.037, respectively, Fig. 2A). These interactions were consisted for both CVD mortality and all-cause mortality with the TyG index (Fig. 2B, C). Notably, among patients without diabetes, an increase in the TyG index was associated with a decreased risk of MACE, while the risk of CVD or all-cause mortality remained unchanged.
Fig. 2.
Subgroup analyses of association between TyG index and and MACE (A), CVD mortality (B) and all-cause mortality (C) in ACS patients who underwent PCI. Black box means HR value, and the bars on both sides of box mean 95% CI of HR
Discussion
To the best of our knowledge, this is the most extensive study to evaluate the association between the TyG index and mortality risk in patients with ACS undergoing percutaneous coronary intervention. We found that the TyG index is a strong predictor of MACE, cardiovascular death and all-cause death in PCI patients with ACS after adjusting for traditional risk factors. Importantly, our analysis did not reveal any non-linear associations between the TyG index and cardiovascular outcomes.
The TyG index was originally developed to detect IR in healthy individuals, serving as a surrogate of the insulin test [6]. Compared to other indicators, it demonstrated the highest AUC value for the early identification of IR [8]. An increase in the TyG index is an early and robust marker of insulin resistance. Moreover, a higher TyG index is associated with decreased insulin sensitivity and an elevated risk of type 2 diabetes.
The association between the TyG index and cardiovascular mortality has been previously explored in both the general population and patients with cardiovascular disease [37, 38]. Otsuka et al. investigated the influence of the TyG index on CCS patients and found that an elevated TyG index could predict the risk of adverse cardiovascular events [40]. Zhang et al. demonstrated that an elevated TyG index could predict adverse outcomes in patients with three-vessel disease [40]. Similarly, He et al. revealed that the TyG index serves as a marker for predicting cardiovascular events in patients undergoing complex coronary artery interventions [41]. Despite these findings, research specifically examining the role of the TyG index in patients with acute coronary syndrome (ACS), particularly those who have undergone percutaneous coronary intervention (PCI), remains limited.
In our study, we enrolled 8019 ACS patients who underwent PCI and examined the relationship between the TyG index and the incidence of cardiovascular events. Our results indicated that the TyG index was significantly associated with an increased risk of MACE and cardiovascular or all-cause mortality during the 1-year follow-up period. These findings strongly suggest that the TyG index is an important prognostic factor for post-PCI patients, particularly those with ACS. The TyG index may serve as a promising target for treatment strategies, independent of other traditional risk factors, across different stages of cardiovascular disease.
As previous studies have reported a U-shaped association between the TyG index and cardiovascular or all-cause mortality in CVD patients with diabetes or pre-diabetes, we conducted non-linear association analysis in our study. However, we found that the TyG index did not show a non-linear association with cardiovascular outcomes, including cardiovascular or all-cause death. This may reflect differences in the study populations included. Determining whether the TyG index can be applied to clinical practice is highly relevant. Our spline analysis demonstrated that the risk of major adverse cardiovascular events gradually increases at values above 10.376, suggesting that this value may serve as a potential risk-stratification threshold for ACS patients after PCI. However, before this cut-off value can be applied in clinical practice, it requires validation in large, prospective cohorts. Furthermore, subsequent randomized controlled trials (RCTs) would be needed to determine whether using this threshold to guide therapy ultimately improves patient outcomes.
Further subgroup analyses revealed reciprocal interactions between the TyG index and gender, smoking status, and ACS diagnosis on cardiovascular events. It’s worth noting that, in our study, an increased TyG index was associated with a reduced risk of MACE, but not with cardiovascular or all-cause mortality in non-diabetes patients. Actually, Li et al. demonstrated that increasing quartiles of the TyG index were associated with a gradedly higher risk of MACCEs (myocardial infarction or stroke) in the general non-diabetic population [42]; however, Drwiła et al. and Jie Yang et al. did not find an association between the TyG index and cardiovascular event risk in non-diabetic patients with MI or after PCI [43, 44]. These results suggest a complex relationship between the TyG index and adverse cardiovascular outcomes in different populations, which needs to be further clarified in future studies, especially non-diabetic populations. We also compared the ability of the TyG index and the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) to predict adverse cardiovascular events. However, neither was a significant predictor of increased risk (Figure S2), which might be partially due to the small sample size of patients who underwent fasting insulin testing. Moreover, the risk of MACE, CVD mortality, and all-cause mortality was lower in the Q3 group than in the Q4 group, suggesting a dose-response relationship that underscores the broad influence of the TyG index.
Several limitations should be acknowledged in this study. Firstly, the study was conducted in ACS patients who underwent PCI from a single center. Further multi-center studies are needed to confirm our findings before these results can be generalized to broader or more diverse populations. Secondly, we cannot rule out the potential for unmeasured confounding factors (e.g., socioeconomic factors) to have influenced the observed association between the TyG index and adverse outcomes. Another limitation of our study is that a non-linear association between the TyG index and cardiovascular events has not been established. This may reflect the complexity of insulin resistance on different disease prognoses, which requires further elucidation in the future.
In conclusion, our study provide evidence that the TyG index is associated with increased risk of MACE, cardiovascular and all-cause death in ACS patients who underwent percutaneous coronary intervention. Baseline measurements of TyG index may be helpful for better prediction of mortality risk in this population. Furthermore, whether TyG index could be an early and powerful intervention target in PCI patients warrant further evaluation in clinical studies.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We would like to thank the staff of the COSTIC study for their contributions to collect samples and follow-up patients for this study. We also want to acknowledge all the participants in this study.
Abbreviations
- TyG index
Triglyceride-glucose index
- PCI
percutaneous coronary intervention
- ACS
acute coronary syndrome
- MACE
major adverse cardiovascular events
- HR
hazard ratio
- CI
confidence interval
- IQR
interquartile range
- CVD
cardiovascular disease
- IR
insulin resistance
- MI
myocardial infarction
- CABG
coronary artery bypass grafting
- PAD
peripheral artery disease
- COPD
chronic obstructive pulmonary disease
- CKD
chronic kidney disease
- TC
total cholesterol
- TG
triglycerides
- HDL-C
high-density lipoprotein cholesterol
- LDL-C
low-density lipoprotein cholesterol
- HbA1c
glycated hemoglobin
- hs-CRP
high-sensitivity C-reactive protein
- BMI
body mass index
- TIMI
thrombolysis in myocardial infarction
- ACE
angiotensin-converting enzyme
- SD
standard deviation
- AUC
area under curve
- HOMA-IR
Homeostatic model assessment of insulin resistance
Author contributions
S.L.H and H.Y.Y. carried out the epidemiological investigation, performed statistical analyses and drafted the manuscript. G.L.C. conceived the study, participated in the research design and edited the final manuscript. Y.S. provided intellectual support for the statistical analyses. All authors have read and approved final vision of the manuscript.
Funding
This study was funded by the National Key Research and Development Program of China (2022YFE0209900) and National Key R&D Program of China (NO. 2017YFC0909400).
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
This study was approved by the institutional review board of Tongji hospital and conducted according to the Declaration of Helsinki. Written informed consent was obtained from all the participants.
Competing interests
The authors declare that they have no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Senlin Hu and Haoyu Yan have equally contributed to this work.
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Associated Data
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Supplementary Materials
Data Availability Statement
No datasets were generated or analysed during the current study.