Abstract
Background
Coronary heart disease (CHD) remains a leading cause of mortality globally. The prognostic value of myeloperoxidase (MPO) and the triglyceride-glucose (TyG) index in predicting adverse cardiovascular events among individuals with CHD remains uncertain. This study aimed to investigate the predictive value of MPO in combination with the TyG index for major adverse cardiovascular events (MACE) in patients with CHD.
Method
A total of 731 patients with CHD admitted to the First Affiliated Hospital of Xinjiang Medical University between July 2022 and January 2024 were enrolled and analyzed. Patients were categorized based on median values of MPO and the TyG index. Subsequent follow-up was conducted to determine the occurrence of MACE within two years of hospital discharge. Multivariate logistic regression analysis was performed to assess the associations between MPO, the TyG index, and MACE. The area under the receiver operating characteristic (ROC) curve (AUC) was utilized to identify the most valuable predictor. Kaplan-Meier curve analysis was employed to examine the relationship between the predictor and prognosis.
Results
263 patients experienced MACE during a median follow-up of two years. Compared to patients with a lower TyG index (<8.44) and MPO (<417 ng/ml), those with a higher combined TyG index and MPO exhibited the highest risk of MACE. Multivariate logistic regression analysis demonstrated that both the TyG index and MPO level were significant predictors of MACE (p < 0.05), with MPO (Odds Ratio [OR] = 1.01, 95 % Confidence Interval [CI] 1.01–1.01) and the TyG index (OR = 2.80, 95 % CI 1.56–5.00) independently associated with increased MACE risk. Kaplan-Meier curves revealed a higher 2-year overall survival rate in CHD patients with lower serum MPO and TyG index levels. ROC curve analysis showed that the AUC for MACE associated with MPO was 0.71, while the AUC for MACE events linked to the TyG index was 0.67. The combined AUC was 0.71, indicating that MPO enhances the predictive efficacy of the TyG index for MACE in CHD patients (p < 0.05).
Conclusion
The TyG index and MPO exhibit a synergistic interaction in increasing the risk of MACE in patients with CHD. These findings underscore the importance of utilizing both measures concurrently when assessing cardiovascular risk in this population.
Keywords: Coronary heart disease, Myeloperoxidase, Triglyceride-glucose index, Major adverse cardiovascular events
Highlights
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The TyG index and MPO exhibit a synergistic interaction in increasing the risk of MACE in patients with CHD. These findings underscore the importance of utilizing both measures concurrently when assessing cardiovascular risk in this population.
1. Background
Coronary heart disease (CHD) is a type of cardiovascular disease characterized by a chronic inflammatory response and plaque accumulation, leading to stenosis or occlusion of the coronary lumen. CHD poses a significant challenge to global public health [1], affecting an estimated 244.11 million individuals worldwide [2,3]. With the rising number of patients with CHD, the incidence of major adverse cardiovascular events (MACE) associated with it has also increased significantly [4,5]. Therefore, accurately predicting the occurrence of MACE in patients with CHD is crucial for developing effective treatment plans and predicting patient outcomes.
The Framingham Risk Score (FRS) has long served as a cornerstone for evaluating cardiovascular disease (CVD) risk [6]. Primarily based on traditional risk factors such as age, cholesterol levels, hypertension, and smoking, the FRS accounts for a substantial portion of CVD risk. However, a significant proportion of individuals, approximately one-third, who exhibit fewer conventional risk factors still develop CVD. Furthermore, in the general population, an alarming 40 % of those with low cholesterol levels experience coronary artery events [[7], [8], [9]]. These findings underscore the presence of unmeasured risk factors in CVD. Consequently, the exploration of a diverse array of additional biomarkers holds significant potential for enhancing the precision of CVD risk assessment [10].
Recent evidence strongly suggests a link between inflammation and insulin resistance (IR) in the development of atherosclerosis. Myeloperoxidase (MPO), a biomarker of inflammatory response, has been shown to be associated with the severity and prognosis of CHD [11,12]. The triglyceride-glucose (TyG) index has emerged as a reliable surrogate marker for assessing IR [13,14], a condition closely associated with CVD [15]. A growing body of research has demonstrated the prognostic significance of the TyG index in predicting the risk of CVD [[16], [17], [18]]. The TyG index has been shown to be an important predictor for major adverse cardiovascular events (MACE) across different nations and various cardiovascular diseases. For instance, the study [19] demonstrated its predictive value in a Turkish population with high cardiovascular risk. Similarly, the research [20] highlighted its significance in predicting long-term outcomes in heart failure patients. These findings underscore the potential utility of the TyG index as a prognostic tool in diverse clinical settings.Early primary prevention studies conducted over three decades ago highlighted the predictive value of inflammation and hyperlipidemia for future cardiovascular events [21,22]. However, limited research has explored the combined prognostic impact of MPO and the TyG index in patients with CHD. Therefore, this study aimed to determine the association between MPO, the TyG index and MACE. Besides, we sought to assess the predictive value of MPO and the TyG index for MACE and identify valuable predictors of incident cardiovascular outcomes in patients with CHD using accessible real-world data.
2. Materials and methods
2.1. Study population
Data from 1142 consecutive patients with CHD admitted to the First Affiliated Hospital of Xinjiang Medical University between July 2022 and January 2024 were enrolled and assessed. Inclusion criteria were [23]: 1) age ≥18 years and <80 years; 2) Criteria for the diagnosis of coronary heart disease:①CT angiography (CTA) or coronary angiography (CAG) shows fixed narrowing of the coronary arteries (diameter stenosis ≥50 %). The affected areas may include the left main artery, left anterior descending artery, left circumflex artery, right coronary artery, diagonal artery, obtuse marginal artery, or posterior descending artery. If any of these arteries has a stenosis ≥50 %, it is considered positive on CAG; otherwise, it is negative.②During angina attacks, electrocardiogram (ECG) monitoring shows signs of myocardial ischemia (such as ST-segment depression or elevation, T-wave inversion) or ECG findings suggestive of stable CHD with repolarization abnormalities.③The patient has typical symptoms related to myocardial ischemia, such as angina (a feeling of chest pressure, dull pain, or discomfort that often radiates to the left shoulder, jaw, or arm). 3) The clinical data is complete.The exclusion criteria were as follows: 1) patients with serious diseases (n = 180), including severe renal or hepatic disease (serum creatinine >1.4 mg/dL or liver function parameters >3 times the upper limit of normal), acute infection and/or inflammation, malignancy, hematologic disease, or autoimmune disease; 2) patients with missing baseline data (n = 200); and 3) participants with ineligible follow-up data (n = 31). Consequently, 731 participants were included in the final analysis. A flowchart of participant selection is presented in Fig. 1. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Review Committee of the First Affiliated Hospital of Xinjiang Medical University. All patients provided written informed consent.
Fig. 1.
Flow diagram illustrating the population selection process.
2.2. Methodologies
A predictive model for the development and prognosis of coronary heart disease based on single nucleotide polymorphisms was constructed through a retrospective cohort study (ChiCTR2300074166). This investigation involved the collection of patient hospitalization data from the electronic medical record system of the First Affiliated Hospital of Xinjiang Medical University. Data recorded included basic patient information, laboratory examination results, percutaneous coronary intervention (PCI) procedure records, and echocardiographic findings for all CHD patients. The TyG index was calculated using the following formula [24]: TyG = Ln[fasting triglyceride (TG) (mg/dL) × fasting plasma glucose (FPG) (mg/dL)/2]. where triglyceride l mmol/L = 88.6 mg/dl and fasting glucose l mmol/L = l/18 mg/dl.
2.3. Follow-up and endpoints
The primary endpoint of this study was the incidence of MACE. These MACE encompassed recurrent angina, non-fatal myocardial infarction (MI), repeat revascularization, acute heart failure (AHF), malignant arrhythmia, non-fatal stroke, cardiogenic shock, and other related conditions. All patients underwent a two-year follow-up period, conducted through outpatient visits, telephone contact, and review of electronic medical records pertaining to rehospitalizations.
2.4. Statistical analysis
Statistical analyses were conducted using SPSS version 26.0. Continuous variables are presented as mean ± standard deviation (SD) or median [interquartile range (IQR)]. Hypothesis testing was performed using the t-test for normally distributed data and the Mann-Whitney U test for non-normally distributed data. Spearman correlation analysis was employed to assess correlations among serum MPO, the TyG index, and established cardiovascular risk factors. Receiver operating characteristic (ROC) curves were generated to evaluate the predictive potential of MPO and the TyG index for MACE. The Kaplan-Meier method was used to analyze the 2-year overall survival rate of CHD patients in the general population. A p-value of less than 0.05 was considered statistically significant.
3. Results
3.1. Baseline characteristics
The study population (n = 731) was stratified into a MACE group (n = 112; 15.3 %) and a MACE-free group (n = 619). Within the study population, the following events were observed: all-cause deaths (n = 7; 6.3 %), recurrent myocardial infarctions (n = 14; 12.5 %), acute heart failure (n = 41; 36.6 %), nonfatal strokes (n = 6; 5.4 %), malignant arrhythmias (n = 17; 15.2 %), and readmissions for angina pectoris (n = 15; 13.4 %). Comparisons of body mass index (BMI), family history of CHD, history of cerebral infarction, white blood cell counts (WBC), neutrophil counts (NE), total cholesterol (TC), TG, low-density lipoprotein cholesterol (LDL-C), FPG, urea, serum creatinine (Scr), direct bilirubin (DBIL), albumin (ALB), left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVEDD), MPO, and TyG index between the two groups revealed statistically significant differences (p < 0.05). Notably, the MACE group exhibited a higher proportion of patients using aspirin, β-blockers, anticoagulants, and entresto compared with the MACE-free group (p < 0.05). The baseline characteristics of the study population are detailed in Table 1.
Table 1.
Baseline characteristics stratified by the occurrence of MACE.
| Variables | MACE-free (n = 619) | MACE (n = 112) | Z/χ2 | p-value |
|---|---|---|---|---|
| Age (years) | 64 (56, 72) | 67 (60, 74) | 0.167 | 0.683 |
| Gender (n %) | ||||
| Male | 432 (69.80) | 76 (67.90) | 0.088 | 0.766 |
| Female | 187 (30.20) | 36 (32.10) | ||
| Ethnicity (n %) | ||||
| Uyghur | 380 (61.40) | 66 (58.90) | 2.507 | 0.474 |
| Han | 152 (24.60) | 28 (25.00) | ||
| Kazakh | 29 (4.70) | 3 (2.70) | ||
| Other | 58 (9.40) | 15 (13.40) | ||
| SBP (mmHg) | 122 (112, 136) | 120 (109, 137) | −0.915 | 0.36 |
| DBP (mmHg) | 75 (68, 83) | 73 (66, 81) | −1.216 | 0.224 |
| HR (bpm) | 75 (67, 83) | 75 (67, 84) | −0.469 | 0.639 |
| BMI (kg/m2) | 25.56 (23.67, 28.35) | 24.62 (22.28, 27.43) | −2.771 | 0.006 |
| Past Medical History | ||||
| Smoking (n %) | 193 (31.20) | 27 (24.10) | 1.931 | 0.165 |
| Alcohol (n %) | 123 (19.90) | 19 (17.00) | 0.343 | 0.558 |
| Hypertension (n %) | 419 (67.70) | 75 (67.00) | 0.002 | 0.967 |
| Diabetes (n %) | 205 (33.10) | 43 (33.10) | 0.954 | 0.329 |
| CHD (n n%) | 129 (20.80) | 13 (11.60) | 4.592 | 0.032 |
| Stroke (n %) | 113 (18.30) | 11 (9.80) | 4.209 | 0.040 |
| Laboratory Test Results | ||||
| WBC (109/L) | 6.38 (5.30, 7.87) | 6.64 (5.80, 8.64) | −2.358 | 0.018 |
| NE (109/L) | 3.52 (2.86, 4.67) | 4.03 (3.13, 5.86) | −2.858 | 0.004 |
| Hb (g/L) | 136 (126, 147) | 136.5 (123, 148.50) | −0.454 | 0.65 |
| TC (mmol/L) | 3.53 (2.97, 4.40) | 3.95 (3.42, 4.60) | −3.998 | <0.001 |
| TG (mmol/L) | 1.07 (0.73, 1.59) | 1.44 (0.89, 2.31) | −4.066 | <0.001 |
| HDL-C (mmol/L) | 0.97 (0.83, 1.17) | 0.96 (0.80, 1.13) | −1.03 | 0.303 |
| LDL-C (mmol/L) | 2.17 (1.71, 2.82) | 2.59 (2.10, 2.94) | −3.89 | <0.001 |
| FPG (mmol/L) | 4.91 (4.27, 5.97) | 5.55 (4.64, 7.53) | −4.109 | <0.001 |
| HbA1c (%) | 6.12 (5.70, 6.80) | 6.2 (5.75, 7.16) | −1.072 | 0.284 |
| Lp(a) (mmol/L) | 140.7 (64.60, 324.43) | 139.4 (66.58, 400.03) | −0.652 | 0.514 |
| PLT (109/L) | 199 (168, 243) | 202 (156.50, 246.00) | −0.197 | 0.844 |
| Urea (mmol/L) | 5.9 (4.90, 7.30) | 6.45 (5.00, 8.59) | −2.620 | 0.009 |
| Scr (umol/l) | 79.7 (67.30, 92.40) | 83.85 (73.30, 99.00) | −3.165 | 0.002 |
| UA (umol/l) | 327.69 (277.50, 387.80) | 334.45 (266.95, 406.73) | −0.427 | 0.669 |
| TBIL (umol/l) | 13.05 (9.70, 17.30) | 17.3 (13.40, 9.50) | −0.658 | 0.510 |
| DBIL (umol/l) | 13.05 (9.70, 17.30) | 13.4 (9.50, 19.47) | −3.081 | 0.002 |
| IBIL (umol/l) | 8.98 (6.20, 12.49) | 8.54 (5.50, 13.28) | −0.364 | 0.716 |
| TP (g/L) | 66.2 (62.80, 69.90) | 65.95 (61.90, 70.30) | −0.212 | 0.832 |
| ALB (g/L) | 41 (38.50, 43.40) | 39.59 (37.03, 42.35) | −3.249 | 0.001 |
| AST (U/L) | 22.6 (18.30, 28.60) | 22.85 (17.45, 26.90) | −0.469 | 0.639 |
| ALT (U/L) | 21.5 (15.37, 29.86) | 19.49 (14.35, 30.80) | −1.147 | 0.251 |
| NT-proBNP (ng/L) | 202 (60.20, 975) | 293 (74.95, 1105.00) | −1.501 | 0.133 |
| LVEF (%) | 61.68 (57.96, 62.95) | 57.42 (42.58, 61.90) | −5.61 | < 0.001 |
| LVEDD (mm) | 49 (47, 51) | 53 (48.00, 59.50) | −5.433 | <0.001 |
| MPO (ng/ml) | 394 (249, 536) | 506 (412.50, 639.00) | −5.903 | <0.001 |
| TyG index | 8.39 (7.95, 8.89) | 8.73 (8.17, 9.44) | −4.696 | <0.001 |
| Discharge medications | ||||
| Aspirin (n %) | 314 (50.70) | 43 (38.40) | 5.291 | 0.021 |
| P2Y12 antagonists (n %) | 161 (26.00) | 35 (31.30) | 1.074 | 0.300 |
| Beta-blockers (n %) | 347 (56.10) | 79 (70.50) | 7.591 | 0.006 |
| Statins (n %) | 466 (75.30) | 76 (67.90) | 2.354 | 0.125 |
| Anticoagulants (n %) | 136 (22.00) | 36 (32.10) | 4.903 | 0.027 |
| Fibrates (n %) | 2 (0.30) | 0 (0.00) | 0.000 | 1.000 |
| Ezetimibe (n %) | 49 (7.90) | 10 (8.90) | 0.030 | 0.862 |
| Alirocumab (n %) | 16 (2.60) | 4 (3.60) | 0.075 | 0.784 |
| Nitrates (n %) | 65 (10.50) | 13 (11.60) | 0.033 | 0.855 |
| Entresto (n %) | 115 (18.60) | 32 (28.60) | 5.290 | 0.021 |
Abbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; BMI, body mass index; WBC, White blood cell; NE, neutrophils; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol; FPG, fasting plasma glucose; PLT, platelet; HbA1c, glycated hemoglobin A1c; SCr, serum creatinine; UA, uric acid; TBIL, total bilirubin; DBIL, direct bilirubin; IBIL, indirect bilirubin; TP, total protein; ALB, albumin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LVEF, left ventricular ejection fraction; LVEDD, left ventricular end-diastolic diameter; MPO, myeloperoxidase; TyG, triglyceride-glucose.
3.2. Baseline characteristics of participants grouped according to MPO level
731 patients were divided into two groups based on the median MPO level, and differences between the groups were subsequently analyzed(Table 2). Compared with patients in the MPO <417 group, patients in the MPO ≥417 group exhibited significantly higher heart rates (HR) and a greater prevalence of combined family history of CHD (p < 0.05). Furthermore, compared with the MPO <417 group, patients in the MPO ≥417 group demonstrated significantly higher levels of WBC count, NE count, platelet count, DBIL, ALB, ghrelin, TC, triglycerides, LDL-C, LDEF, and TyG indices. The number of cases of aspirin, P2Y12 antagonist, and β-blocker use was higher in the group of patients with MPO≥417 after discharge (p < 0.05).
Table 2.
Clinical characteristics of patients grouped by MPO.
| Variables | MPO<417 (n = 365) | MPO≥417 (n = 366) | Z/χ2 | p-value |
|---|---|---|---|---|
| Age (years) | 65 (57, 75) | 64 (56, 71) | −1.747 | 0.081 |
| Gender (n %) | ||||
| Male | 253 (68.80) | 255 (70.20) | 0.129 | 0.719 |
| Female | 115 (31.30) | 108 (29.80) | ||
| Ethnicity (n %) | ||||
| Uyghur | 249 (67.70) | 197 (54.30) | 16.160 | 0.001 |
| Han | 71 (19.30) | 109 (30.00) | ||
| Kazakh | 12 (3.30) | 20 (5.50) | ||
| Other | 36 (9.80) | 37 (10.20) | ||
| SBP (mmHg) | 123 (113.50, 135) | 121 (110, 138) | −0.929 | 0.353 |
| DBP (mmHg) | 75 (68, 82) | 75 (68, 82) | −0.126 | 0.900 |
| HR (bpm) | 74 (67, 82) | 76 (68, 86) | −2.920 | 0.003 |
| BMI (kg/m2) | 25.34 (23.53, 27.83) | 25.53 (23.23, 28.70) | −0.367 | 0.714 |
| Past Medical History | ||||
| Smoking (n %) | 109 (29.60) | 111 (30.60) | 0.041 | 0.840 |
| Alcohol (n %) | 71 (19.30) | 71 (19.60) | 0.000 | 1.000 |
| Hypertension (n %) | 251 (68.20) | 243 (66.90) | 0.082 | 0.775 |
| Diabetes (n %) | 131 (35.60) | 117 (32.20) | 0.780 | 0.377 |
| CHD (n %) | 87 (23.60) | 55 (15.20) | 8.478 | 0.004 |
| Stroke (n %) | 68 (18.50) | 56 (15.40) | 1.001 | 0.317 |
| Laboratory Test Results | ||||
| WBC (109/L) | 5.97 (4.97, 7.08) | 6.95 (5.91, 8.58) | −7.828 | <0.001 |
| RBC (109/L) | 4.44 (4.06, 4.82) | 4.48 (4.14, 4.86) | −1.433 | 0.152 |
| Neutrophils (109/L) | 3.26 (2.61, 4.12) | 4.12 (3.20, 5.63) | −8.365 | <0.001 |
| LY (%) | 1.87 (1.42, 2.37) | 1.84 (1.32, 2.44) | −0.257 | 0.797 |
| PLT (109/L) | 190.50 (165, 231) | 211 (174, 253) | −3.865 | <0.001 |
| Hb (g/L) | 136 (126, 148) | 136 (125, 148) | −0.144 | 0.885 |
| Urea (mmol/L) | 5.90 (5.00, 7.19) | 6.20 (4.90, 7.70) | −1.752 | 0.080 |
| Scr (umol/l) | 80.15 (69.00, 92.84) | 80.56 (68, 93.72) | −0.495 | 0.620 |
| UA (umol/l) | 327.67 (277.25, 387.56) | 328.80 (273.82, 396.79) | −0.157 | 0.875 |
| TBIL (umol/l) | 12.90 (9.45, 16.80) | 13.27 (9.90, 18.62) | −1.638 | 0.101 |
| DBIL (umol/l) | 3.10 (1.81, 4.31) | 2.58 (0.30, 4.07) | −3.744 | <0.001 |
| IBIL (umol/l) | 9.01 (6.16, 12.37) | 8.69 (6.16, 13.04) | −0.198 | 0.843 |
| TP (g/L) | 65.88 (62.60, 69.50) | 66.30 (62.90, 70.40) | −1.823 | 0.068 |
| ALB (g/L) | 41.30 (38.99, 43.65) | 40.30 (37.38, 42.80) | −4.152 | <0.001 |
| AST (U/L) | 22.58 (17.65, 27.01) | 22.90 (18.60, 29.72) | −1.888 | 0.059 |
| ALT (U/L) | 20.28 (14.48,28.71) | 22.38 (15.83,32.00) | −2.403 | 0.016 |
| TC (mmol/L) | 3.48 (2.99, 4.31) | 3.67 (3.09, 4.51) | −2.576 | 0.010 |
| TG (mmol/L) | 1.03 (0.69, 1.48) | 1.23 (0.82, 1.86) | −4.177 | <0.001 |
| HDL-C (mmol/L) | 0.98 (0.85, 1.17) | 0.96 (0.82, 1.86) | −1.589 | 0.112 |
| LDL-C (mmol/L) | 2.13 (1.69, 2.78) | 2.33 (1.82, 2.94) | −2.841 | 0.004 |
| Lp(a) (mmol/L) | 140.33 (62.17, 301.71) | 141.40 (66.15, 355.37) | −0.847 | 0.397 |
| FPG (mmol/L) | 4.96 (4.30, 6.01) | 4.96 (4.36, 6.32) | −0.792 | 0.428 |
| HbA1c (%) | 6.15 (5.70, 6.90) | 6.10 (5.70, 6.86) | −0.040 | 0.968 |
| LVEF (%) | 61.90 (58.84, 62.95) | 61.04 (54.00, 62.95) | −2.569 | 0.010 |
| LVEDD (mm) | 49 (47, 52) | 49 (47, 53) | −1.191 | 0.234 |
| NT-proBNP (ng/L) | 202 (63.40, 880.00) | 226 (62.40, 1120) | −0.441 | 0.659 |
| MPO(ng/ml) | 269.5 (193, 355) | 569 (484, 686) | −23.398 | <0.001 |
| TyG index | 8.31 (7.92, 8.82) | 8.55 (8.09, 9.10) | −4.031 | <0.001 |
| Discharge medications | ||||
| Aspirin (n %) | 158 (42.90) | 199 (54.80) | 9.862 | 0.002 |
| P2Y12 antagonists (n %) | 84 (22.80) | 112 (30.90) | 5.599 | 0.018 |
| Beta-blockers (n %) | 187 (50.80) | 239 (58.30) | 16.354 | <0.001 |
| Statins (n %) | 269 (73.10) | 273 (75.20) | 0.321 | 0.571 |
| Anticoagulants (n %) | 90 (24.50) | 82 (22.60) | 0.258 | 0.612 |
| Fibrates (n %) | 2 (0.50) | 0 (0.00) | 0.488 | 0.485 |
| Ezetimibe (n %) | 34 (9.20) | 25 (6.90) | 1.064 | 0.302 |
| Alirocumab (n %) | 8 (2.20) | 12 (3.30) | 0.506 | 0.477 |
| Nitrates (n %) | 63 (17.10) | 84 (23.10) | 3.757 | 0.053 |
| Entresto (n %) | 43 (11.70) | 35 (9.60) | 0.600 | 0.439 |
Abbreviations: LY, Lymphocyte.
3.3. Baseline characteristics of participants grouped according to the TyG index
Based on the TyG index, 731 patients were divided into two groups. Patients with a TyG index ≥8.44 exhibited higher age, DBP, HR, and BMI compared to those with a TyG index <8.44. A greater number of patients in the TyG index ≥8.44 group had a history of diabetes mellitus (p < 0.05). Compared with the TyG index <8.44 group, the TyG index ≥8.44 group had higher levels of WBC count, neutrophil count, PLT count, blood uric acid (UA), DBIL, indirect bilirubin (IBIL), total protein (TP), alanine aminotransferase (ALT), TC, TG, high-density lipoprotein cholesterol (HDL-C), LDL-C, FPG, glycosylated hemoglobin (HbA1c), MPO. A greater number of patients in the TyG index ≥8.44 group were prescribed aspirin, P2Y12 antagonists, and β-blockers (p < 0.05). The above data are presented in Additional file: Table S1.
3.4. Correlation between the MPO, TyG index and CHD risk factors
The analysis of associations between MPO, TyG index, and risk factors for coronary heart disease using Spearman correlation is presented in Table 3. The analysis revealed a positive correlation between MPO and TyG index, as well as with TC, TG, and LDL-C. Additionally, MPO showed a negative correlation with patient age (r = −0.088, p = 0.017) and HDL-C (r = −0.079, p = 0.032). The TyG index also exhibited positive correlations with MPO, FPG, TC, TG, LDL-C, BMI, DBP, and UA, while showing negative correlations with patient age (r = −0.130, p < 0.001) and HDL-C (r = −0.160, p < 0.001).
Table 3.
Correlations between the MPO, TyG index and CHD risk factors.
| Variables | MPO |
TyG index |
||
|---|---|---|---|---|
| Correlation coefficient(r) | p-value | Correlation coefficient(r) | p-value | |
| MPO (ng/ml) | – | – | 0.176 | <0.001 |
| TyG index | 0.176 | <0.001 | – | – |
| HbA1c (%) | 0.009 | 0.817 | – | – |
| FPG (mmol/L) | 0.046 | 0.217 | 0.627 | <0.001 |
| TC (mmol/L) | 0.074 | 0.044 | 0.297 | <0.001 |
| TG (mmol/L) | 0.180 | <0.001 | 0.908 | <0.001 |
| HDL-C (mmol/L) | −0.079 | 0.032 | −0.160 | <0.001 |
| LDL-C (mmol/L) | 0.094 | 0.011 | 0.198 | <0.001 |
| Lp (a) (mmol/L) | 0.016 | 0.667 | −0.070 | 0.059 |
| Age (years) | −0.088 | 0.017 | −0.130 | <0.001 |
| BMI (kg/m2) | 0.032 | 0.394 | 0.124 | 0.001 |
| SBP (mmHg) | −0.016 | 0.665 | 0.071 | 0.055 |
| DBP (mmHg) | 0.009 | 0.813 | 0.122 | 0.001 |
| Scr (umol/l) | 0.041 | 0.272 | 0.003 | 0.934 |
| UA (umol/l) | −0.004 | 0.908 | 0.142 | <0.001 |
3.4.1. Single and multiple factorial logistic regression analysis affecting MACE events
A multivariate logistic regression model was used, incorporating factors found to be statistically significant for MACE in univariate analyses. The results showed MPO, the TyG index, WBC and LVEDD as independent risk factors for the combined outcome of CHD and MACE (p < 0.05) (Table 4).
Table 4.
Univariate and Multivariate Logistic regression analyses for MACE.
| Variables | Univariate |
Multivariate |
||||
|---|---|---|---|---|---|---|
| OR | 95 %CI | p value | OR | 95 %CI | p value | |
| MPO (ng/ml) | 1.01 | (1.01–1.01) | <0.001 | 1.01 | (1.01–1.01) | <0.001 |
| TyG | 1.95 | (1.51–2.52) | <0.001 | 2.80 | (1.56–5.00) | <0.001 |
| Gender (n %) | 0.91 | (0.59–1.41) | 0.683 | |||
| Age (years) | 1.02 | (1.01–1.04) | 0.014 | |||
| SBP (mmHg) | 1.00 | (0.99–1.01) | 0.549 | |||
| DBP (mmHg) | 0.99 | (0.98–1.01) | 0.516 | |||
| BMI (kg/m2) | 0.93 | (0.88–0.98) | 0.011 | 0.88 | (0.78–0.99) | 0.038 |
| Smoking (n %) | 0.70 | (0.44–1.12) | 0.135 | |||
| Alcohol (n %) | 0.82 | (0.48–1.40) | 0.475 | |||
| Hypertension (n%) | 0.97 | (0.63–1.48) | 0.880 | |||
| Cancer (n %) | 0.82 | (0.24–2.82) | 0.758 | |||
| Stroke (n %) | 0.49 | (0.25–0.94) | 0.032 | 0.12 | (0.03–0.46) | 0.002 |
| COPD (n %) | 0.96 | (0.33–2.83) | 0.941 | |||
| TDM (n %) | 1.26 | (0.83–1.91) | 0.279 | |||
| CHD (n %) | 0.50 | (0.27–0.92) | 0.025 | |||
| WBC (109/L) | 1.12 | (1.04–1.21) | 0.002 | 1.52 | (1.01–2.30) | 0.044 |
| RBC (109/L) | 0.72 | (0.51–1.02) | 0.062 | |||
| NE (%) | 1.15 | (1.06–1.24) | <0.001 | 0.56 | (0.36–0.87) | 0.010 |
| BUN (mmol/L) | 1.12 | (1.06–1.19) | <0.001 | |||
| Scr (umol/l) | 1.01 | (1.01–1.01) | <0.001 | |||
| ALB (g/L) | 0.92 | (0.88–0.97) | 0.001 | |||
| ALT (U/L) | 1.00 | (0.98–1.01) | 0.508 | |||
| AST (U/L) | 1.00 | (1.00–1.01) | 0.247 | |||
| LDL-C (mmol/L) | 1.49 | (1.18–1.89) | <0.001 | |||
| NTProBNP (ng/L) | 1.00 | (1.00–1.00) | 0.205 | |||
| LVEDD (mm) | 1.07 | (1.05–1.10) | <0.001 | 1.07 | (1.01–1.13) | 0.016 |
| LVEF (%) | 0.93 | (0.91–0.95) | <0.001 | |||
| Anticoagulants (n%) | 1.68 | (1.08–2.61) | 0.020 | |||
| Aspirin (n %) | 0.61 | (0.40–0.91) | 0.017 | |||
| Statins (n %) | 0.69 | (0.45–1.07) | 0.100 | |||
| P2Y12 antagonists (n %) | 1.29 | (0.83–2.00) | 0.250 | |||
Abbreviations: BUN, blood urea nitrogen.
3.5. ROC curve analysis
The predictive value of MPO and TyG index at admission for adverse cardiovascular events in patients with CHD was evaluated (Fig. 2). ROC curve analysis demonstrated an AUC of 0.71 (95 % CI 0.63–0.72) for MPO in predicting MACE, and an AUC of 0.67 (95 % CI 0.63–0.72) for the TyG index. Combining MPO with the TyG index yielded an AUC of 0.71, suggesting that MPO improves the predictive capacity of the TyG index for MACE in patients with CHD.
Fig. 2.
Receiver operating characteristic curves of the MPO (A), TyG index (B) and combined MPO and TyG index (C).
3.6. Kaplan ⁃ Meier survival analysis
Kaplan–Meier survival curves were generated to illustrate patient outcomes stratified by varying levels of the TyG index and MPO (Fig. 3). As demonstrated in Supplementary Fig. S1 and S2, a significant incremental increase in the cumulative incidence of MACE was observed across tertiles of both the TyG index and MPO (p < 0.001). Patients with elevated levels of MPO, TyG index, or both exhibited the highest risk of cardiovascular events, whereas the lowest risk was observed in patients with low levels of both indices (p < 0.001).
Fig. 3.
Cumulative Kaplan-Meier curves of the MPO combined with the TyG index in the total population
Q1: MPO ≥417 ng/ml and the TyG index ≥8.44; Q2: MPO <417 ng/ml and the TyG index ≥8.44;
Q3: MPO <417 ng/ml and the TyG index ≥8.44; Q4: MPO <417 ng/ml and the TyG index <8.44.
4. Discussion
This study, to the best of our knowledge, represents the first investigation into the relationship between the TyG index, MPO and MACE in patients with CHD. The principal findings were: (1) both the TyG index and MPO were independently associated with an increased risk of MACE in CHD patients after adjusting for traditional cardiovascular risk factors; (2) the AUC for MACE prediction was 0.67 for the TyG index and 0.71 for MPO, with the combination of both achieving an AUC of 0.71, demonstrating that MPO enhances the predictive capacity of the TyG index. Specifically, the combination of a TyG index ≥8.44 and MPO ≥417 effectively identified individuals at the highest risk for MACE within the CHD population. These results establish the prognostic value of the TyG index and MPO for MACE in patients with CHD.
CHD is the leading cause of death from cardiovascular diseases. MACE events are the most frequent composite endpoint used in CVD trials to assess the safety and efficacy of CVD-related interventions and treatments [25]. In a study following 731 CHD patients for two years, 112 patients (15.32 %) experienced MACE, highlighting the high risk of MACE in CHD patients and the importance of implementing effective measures for accurate prognosis evaluation. Insulin resistance is a general term encompassing low insulin responsiveness in adipose tissue, skeletal muscle, liver, and pancreas. Theoretically, IR can worsen atherosclerosis through mechanisms involving systemic inflammation, endothelial dysfunction, and oxidative stress [26,27]. The TyG index, derived from FPG and TG, is commonly used as a surrogate marker of IR [28]. Prior research has shown that a high TyG index is a risk factor for CVD [29,30]. Additionally, elevated TyG index levels were independently linked to a greater risk of MACE in patients undergoing coronary artery bypass grafting [31]. A comprehensive analysis identified the TyG index as an independent predictor of both long-term all-cause mortality (hazard ratio [HR] = 1.64, 95 % CI 1.06–2.54) and MACE (HR = 1.36, 95 % CI 1.05–1.95) [32]. Consistent with these findings, the current study demonstrated a significantly higher TyG index in the MACE group compared to the non-MACE group (p < 0.05). Furthermore, the TyG index emerged as an independent risk factor for MACE in CHD patients (OR = 2.80, 95 % CI 1.56–5.00). In conclusion, an elevated TyG index is associated with an increased risk of MACE in patients with CHD.
MPO, a crucial enzyme in reactive oxygen species generation, plays a significant role in inflammation and oxidative stress associated with cardiovascular conditions [33,34]. Elevated MPO activity can induce lipid peroxidation, contributing to arterial endothelial damage and subsequent plaque formation [35,36]. Furthermore, MPO may indirectly influence CAD development by regulating inflammatory cell recruitment and activation [37,38]. Several studies report that circulating MPO levels independently predict MACE in patients with chest pain, acute coronary syndrome, or AMI [[39], [40], [41], [42], [43]]. Previous research has demonstrated an association between MPO and various cardiovascular conditions, including coronary artery disease [44], congestive heart failure [45], and venous thrombosis [34]. Brennan et al. [40] evaluated the relationship between MPO and cardiovascular events in 604 patients with chest pain, finding that higher MPO levels predicted MACE (myocardial infarction, death, or revascularization) within 30 days and 6 months. Heslop et al. [46] indicated an association between MPO and the risk of cardiovascular disease during up to 13 years of follow-up. Wong et al. [47]investigated the correlation between MPO levels and the risk of developing cardiovascular events in 1302 healthy adults over 3.8 years. In the present study, the MPO level in the MACE group was significantly higher than that in the non-MACE group (p < 0.05), and MPO was an independent risk factor for MACE (OR = 1.01, 95 % CI 1.01–1.01). These findings suggest that MPO may contribute to the occurrence of MACE in CHD patients, potentially through mechanisms involving the promotion of atherosclerosis and the exacerbation of vascular injury.
In this study, we found that MPO and the TyG index are significantly linked to the risk of MACE. This suggests they might be useful as biomarkers for assessing cardiovascular risk. But when we combined MPO and the TyG index into a model, it only improved prediction a little bit (the AUC went from 0.67 to 0.71). It didn't beat existing clinical risk scores like the GRACE score or the TIMI risk score. These existing scores are already well-tested, easy to use, and give doctors quick guidance.
In contrast, our new approach with these new biomarkers faces some practical challenges. For example, testing for MPO and the TyG index needs extra lab work and time, which could make things more complicated and expensive in real-life medical practice. Also, since our study was done at just one place and looked back at past data, there might be other factors we didn't account for that could affect the results. This makes it harder to say that our approach would work everywhere.
Even so, we think combining MPO and the TyG index still has some potential value. The fact that it didn't improve predictions a lot might be because our sample size was too small or because of how we designed the study. If we did the study with more people and at multiple centers, we could get a better idea of how well MPO and the TyG index work in different groups of people. For example, people of different races, genders, or ages might respond differently to these biomarkers. By doing multicenter studies, we could figure out these differences and make more tailored risk assessment models. This could give doctors better tools to identify high-risk patients early and improve their outcomes. It could also help design better clinical trials by finding the right patients to include.So, future research should test the combined use of these biomarkers in larger, multicenter, and forward-looking studies to see if they can really improve cardiovascular risk assessment.
Combining parameters like MPO and TyG index helps predict cardiovascular risks in coronary heart disease patients. MPO shows inflammation, and TyG index reflects insulin resistance and metabolism issues. But integrating more factors (MPO, TyG, age, kidney function, etc.) is challenging. We need a standardized scoring system to calculate risk scores, enabling precise individual risk assessment and better clinical decisions to improve patient outcomes.
In our research, combining ECG and laboratory tests is important for clinical decision-making, especially in complex cases. Studies have shown this integrated approach is effective. Hayıroglu et al. [48] developed an ECG-based diastolic index to quickly predict heart diastolic dysfunction, which is simple and cost-effective. Cicek et al. [49] used deep learning to combine ECG and lab data to predict short-term mortality in acute pulmonary embolism patients, offering a new way to fuse multimodal data. These studies show that comprehensive models can improve disease diagnosis and prognosis accuracy. While our current focus is on MPO and the TyG index, we plan to introduce similar integrated methods in the future to enhance the clinical value of our research.
Several limitations of the present study should be acknowledged. Firstly, this investigation is a single-center retrospective study with a relatively small sample size, which may introduce potential biases into the study results. Future research should involve multi-center studies with larger sample sizes to validate the findings and enhance the accuracy of CHD prognosis assessment. Secondly, due to the observational nature of this study, a causal relationship between the TyG index, inflammatory biomarkers, and cardiovascular risk cannot be definitively established. Thirdly, the possibility of residual or unmeasured confounding biases, which may influence the estimation of effect sizes, cannot be entirely excluded.
5. Conclusion
In summary, patients with CHD exhibit a high risk of MACE. Elevated levels of the TyG index and MPO have been identified as independent risk factors for MACE in this population. Both the TyG index and MPO can serve as valuable auxiliary indicators for early prognostic assessment in CHD patients, thereby guiding timely clinical interventions.
CRediT authorship contribution statement
Adila Wulamu: Writing – original draft. Xiao-Lei Li: Methodology. Munawaer Keremu: Data curation. Shu-Ying Ding: Data curation. Aibibanmu Aizezi: Software. Yan-Peng Li: Writing – original draft. Gulihuma Abudukeranmu: Data curation. Fen Liu: Software. Xia li: Data curation. Xiao-Mei Li: Supervision. Yi-Tong Ma: Supervision. Dilare Adi: Writing – review & editing. Adila Azhati: Writing – review & editing.
Data availability statement
The datasets supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committees of the First Affiliated Hospital of Xinjiang Medical University. No informed consent was required.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Funding
This study was supported by grants from the Open project of Key Laboratory from Science and Technology Department of Xinjiang Uygur Autonomous Region (2022D04019). Tianshan Talent Training Program of Xinjiang Uygur Autonomous Region (2023TSYCCX0052).
Declaration of competing interest
The authors declare that they have no competing interests.
Acknowledgements
We thank all the investigators and subjects who participated in this project.
Handling Editor: Dr D Levy
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijcrp.2025.200490.
Contributor Information
Dilare Adi, Email: dil515@sina.com.
Adila Azhati, Email: adlndl@163.com.
Appendix A. Supplementary data
The following is the Supplementary data to this article.
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This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets supporting the conclusions of this article will be made available by the authors, without undue reservation.