2.1. Study Population and Design

This study was derived from a prospective, real-world, single-center cohort of consecutive STEMI patients undergoing PCI in the General Hospital of Northern Theater Command from January 2015 to April 2017. The inclusion criteria were STEMI patients receiving PCI aged ≥ 18 years. The main exclusion criteria included KYNA data could not be obtained and co-occurrence of one or more disorders or conditions (autoimmune disorders, active inflammatory conditions, severe heart failure, suspected myocarditis or pericarditis, pathologies of the hematopoietic system, advanced renal or hepatic complications, and/or immunosuppressive therapy). Blood samples were collected upon arrival at the emergency department. Quantitative determination of plasma KYNA levels were carried out using ultraperformance liquid chromatography (UPLC)/Q-TOF analysis. During the hospitalization, therapeutic management was made by the attending physicians based on current guidelines. The study protocol (Ethics Review No. 09 [2013]) was formally approved by the Ethics Committee at the General Hospital of Northern Theater Command. This study complied with the provisions of the Declaration of Helsinki. Written informed consent was obtained from all the involved participants.

2.2. Metabolomics Analysis Based on ¹H-Nuclear Magnetic Resonance (NMR)

The metabolic profiles of 50 STEMI patients and 50 control subjects were conducted by the Shanghai Metabolome Institute using ¹H-NMR analysis [13].

2.3. Targeted Analysis for KYNA by LC-MS/MS

Before analysis, frozen samples were first brought to ambient temperature. Two hundred microliters of the supernatant from each sample was carefully evaporated to dryness under a gentle stream of nitrogen maintained at 35°C. The dried residues were revitalized by the addition of 50 mL of a 50:50 (v/v) acetonitrile–water mixture and then filtered through a 0.22-micron membrane to remove the potential particulate matter. For chromatographic separation, the samples were analyzed utilizing an UPLC system equipped with a high-strength silica C18 column, coupled with a Q-TOF from Waters Corporation [13].

2.4. Outcomes

The primary outcome was major adverse cardiovascular events (MACEs) within 12 months after the procedure, defined as a composite of all-cause mortality, recurrent MI, and heart failure. Secondary outcomes included the components of MACE within 12 months. Clinical follow-up was conducted at 3, 6, 9, and 12 months by phone calls, outpatient visits, or readmission by the research staff.

2.5. Thrombus Tissue From the Culprit Vessels in STEMI Patients

Thrombus tissue specimens were acquired from the culprit vessel via the thrombectomy catheters (export thrombectomy catheter; Medtronic, Minneapolis, Minnesota, United States) in STEMI patients.

2.6. Human Peripheral Blood Monocyte Isolation Procedure

Peripheral blood mononuclear cells (PBMCs) were extracted from STEMI patients and control subjects via density gradient centrifugation using a Ficoll-based separation solution to ensure efficient purification. Furthermore, we detected the KAT1 expression in PBMCs by western blot.

2.7. Immunohistochemical and Immunofluorescence Staining Analysis

Paraffin-embedded specimens were analyzed by immunofluorescence staining. The specimens were incubated with primary anti-KAT1 antibody overnight at 4°C. Staining was performed using a VECTASTAIN Elite ABC kit (ZSGB-Bio Catalog No. 9720, China) following the manufacturer’s instructions.

2.8. Statistical Analysis

The sample size was calculated to achieve the objective of determining the relationship between identified metabolites and the risk of the MACEs at 12 months after the procedure. The sample size was determined based on the study plan, which called for the inclusion of five prediction variables (anticipated) in a Cox proportional hazards regression model for MACE at 12 months after the procedure. Given that we would include five variables in our model, we required at least 50 MACEs. Thus, we proposed a total sample size of 1000 STEMI patients. This would ensure a stable model, as we would have 10 events per variable in the multivariable analyses, based upon our anticipated 5.0% event rate in the STEMI participants. The enrolled participants were stratified into tertiles based on plasma KYNA levels. Continuous variables were presented as mean values ± standard deviation or median values along with interquartile ranges (IQRs; Q1 and Q3) and compared by analysis of variance (ANOVA) or the nonparametric Kruskal–Wallis H test, contingent upon the data distribution. Categorical variables were expressed as frequencies (proportions) and compared using the chi-squared test or Fisher’s exact test for small sample sizes. We used the Kaplan–Meier method to estimate the rates of time-to-event occurrence; comparisons between groups were performed using the log-rank test. We used the Cox proportional hazards regression models to determine the hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) and scrutinized the correlations between plasma KYNA levels and clinical outcomes. For multivariate analysis, adjusted for selected patient characteristics and medical factors include age, sex, presence of hypertension, diabetes mellitus, smoking status, prior history of MI, previous PCI, earlier stroke incidents, familial history of AMI, N-terminal pro-B-type natriuretic peptide levels, left ventricular ejection fraction, hemoglobin count, serum creatinine concentration, procedural details (e.g., transradial access and the number of treated coronary arteries), and prescribed medications including P2Y₁₂ inhibitors, aspirin, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, statins, and beta-blockers. The receiver operating characteristic (ROC) curve for plasma KYNA was generated to evaluate its accuracy in predicting 12-month MACE and all-cause mortality, with the discriminatory power measured by the area under the curve (AUC). The evolving discriminatory capability of KYNA for predicting MACE was assessed using a time-dependent ROC analysis. We used a restricted cubic spline (RCS) regression model to investigate the potential nonlinear relationship between plasma KYNA and the likelihood of 12-month MACE. Statistical analyses were performed using SAS software (Version 9.4; SAS Institute, Cary, North Carolina, United States). Unless otherwise specified, a two-sided p < 0.05 was considered to indicate statistical significance.

3.1. Study Population

We identified 31 key metabolites (Figure S1) in 50 STEMI patients and 50 control subjects, including sugars, amino acids, organic acids, alcohols, nucleotides, and lipids. Compared to control subjects, 15 significantly different metabolites were identified in the plasma of STEMI patients, comprising 10 amino acids, 3 organic acids, glucose-related compounds, and 1 alcohol compound. Pyroglutamic acid, 3-hydroxybutyric acid, alpha-glucose, and salicylic acid levels were elevated in STEMI patients. On the coronary, glutamic acid, tryptophan, histidine, sorbitol, formic acid, isoleucine, tyrosine, leucine, valine, glycine, and alanine levels were downregulated in STEMI patients. Among these amino acids, glutamic acid, tryptophan, and histidine had greater absolute correlation coefficients, contributing more significantly to the differences between control subjects and STEMI patients. Further study showed there were significant differences in KYNA levels between the control subjects and the STEMI patients (Figure 1).

The study included 973 STEMI patients with the median KYNA plasma concentration of 337.67 nmol/L (IQR, 241.16–500.29 nmol/L). The recruited STEMI patients’ baseline characteristics, procedural information, and medication results for the KYNA tertiles were shown in Table 1. The mean age of the STEMI patients was 59.23 ± 12.31 years; males comprised 80.06%. The proportions of these patients with hypertension and diabetes were 57.30% and 31.20%, respectively. Patients with elevated KYNA levels also had higher plasma NT-proBNP levels. Medical treatment was similar among control subjects and STEMI patients (Table 1).

3.2. Clinical Outcomes

Nine hundred and seventy-three STEMI patients with a 98.18% follow-up rate at 12 months were finally included in the study. The principal outcomes were presented in Table 2. The primary endpoint of 12-month MACE was observed in 5 individuals (1.54%), 12 (3.70%), and 33 (10.15%) in Tertiles 1, 2, and 3 of the KYNA group, respectively, revealing a statistically significant difference (p < 0.001). A significant difference was found in the frequencies of all-cause mortality (0.93% vs. 2.78% vs. 7.08%, p < 0.001) and heart failure (0.62% vs. 0.31% vs. 2.77%, p = 0.008) at 12 months among the KYNA tertiles. A Kaplan–Meier curve for the cumulative incidence of MACE at 12 months was shown in Figure 2. The plasma KYNA levels were strongly associated with increased risk of MACE (adjusted HR: 1.020; 95% CI: 1.011–1.029; p < 0.001) and all-cause mortality (adjusted HR: 1.023; 95% CI: 1.012–1.033; p < 0.001) by the multivariable analyses. Furthermore, the plasma KYNA levels were associated with MACE (highest KYNA tertile vs. lowest KYNA tertile, adjusted HR: 5.95, 95% CI: 2.03–17.44; p = 0.0012) and all-cause mortality (adjusted HR: 7.11, 95% CI: 1.52–33.29; p = 0.013) (Table 3). RCS regression analysis showed that KYNA levels were associated with 12-month MACE without a nonlinear correlation (overall p < 0.001; nonlinearity: p = 0.57) (Figure 3).

3.3. Predictive Performance of KYNA

The KYNA level showed moderate predictive value for 12-month MACE (AUC = 0.72, 95% CI: 0.65–0.80) and all-cause mortality (AUC = 0.74, 95% CI: 0.65–0.83) (Figure 4a). Time-varying ROC analysis showed that the KYNA level could robustly and reliably predict the risk of MACE within 12 months (Figure S2A). The AUCs for the 3-, 6-, 9-, and 12-month MACE were 0.79, 0.75, 0.74, and 0.72, respectively (Figure S2B). Sensitivity analyses consistently verified the association between elevated KYNA levels and the risk of MACE within a 1 year, as depicted in Figure 4b.

3.4. High KAT1 Expression in Coronary Artery Thrombi Tissues

Thrombectomy catheters were employed to extract multiple thrombotic specimens from the culprit coronary arteries, which subsequently underwent immunofluorescence staining analysis to investigate the colocalization of CD68-positive macrophages with KAT1 expression, as illustrated in Figure 5.

3.5. KAT1 Expression in PBMCs From STEMI Patients

KAT1 expression obviously upregulated in PBMCs derived from STEMI patients was a 1.43-fold increase compared with the control subjects by western blot analysis. These findings underscored the pivotal pathological role of KAT1 in the development of STEMI, as depicted in Figure 6.

3.6. KAT1 Expression Increased in Cardiac Tissues After MI

The expression of KAT1 increased in cardiac tissues following MI. Immunofluorescence staining showed KAT1 expression in myocardial tissue at the infarct border zone of the heart tissue at different time points after MI in mice. Immunofluorescence staining showed that KAT1 expression in the infarcted area was significantly increased compared to the noninfarcted area at 1, 3, 7, 14, and 28 days after MI, especially from Days 1 to 3. Moreover, western blot analysis revealed that KAT1 expression in the infarcted area gradually increased and then decreased after 1, 3, and 7 days following MI (Figure 7).

4.1. Limitations

This study has several potential limitations. First, it is a post hoc analysis stemming from a single-center, prospective cohort comprising STEMI patients undergoing PCI. Despite the sample size being considered in advance and the results being adjusted using regression, there is still potential for overestimated sample size and unmeasured confounding, respectively. Second, plasma KYNA levels were measured at admission to the emergency department. Dynamic monitoring of KYNA might predict long-term risks better. Furthermore, the time-varying ROC analysis verified the KYNA levels at admission, providing a reliable and convenient tool for predicting the risk of MACE within 12 months in STEMI patients. As with all biomarkers, prospective evaluations are desirable to demonstrate their usefulness and effectiveness in clinical practice. Further functional metabolomics research, involving the discovery of KYNA through metabolite profiling and functional analysis using biological approaches, employs a bedside-to-bench strategy to translate clinical findings into laboratory studies. This approach aims to bridge the gap between identified biomarkers and their mechanistic relevance to disease pathogenesis.