PCI postoperative MACE risk prediction marker for acute ST segment elevation type myocardial infarction patient

Abstract

The invention discloses a PCI postoperative MACE risk prediction marker for an acute ST segment elevation type myocardial infarction patient, and belongs to the field of biological medicine. The application comprises application of a reagent for detecting the lipid metabolism marker in preparation of a kit for predicting the risk of main adverse cardiovascular events after the PCI operation of a patient suffering from the acute ST segment elevation type myocardial infarction, and the reagent for detecting the lipid metabolism marker comprises glycodeoxycholic acid, [(2R)-3-[(6Z, 9Z, 12Z)-octadeca-6, 9, 10-trimethyl-1, 3, 5-trimethyl-1, 3, 5-trimethyl-1, 3, 5-trimethyl-1, 3, 5-trimethyl-1, 3, 5-trimethyl-1, 3, 5-trimethyl-1, 3, 5-trimethyl-1, the detection reagent is selected from one or more of a detection reagent for N-((2S, 3R)-1, 3-dihydroxy octadecane-4-alkene-2-yl) acetamide, a detection reagent for 2-(2, 6, 7, 12-trienoyl) oxy-2-[(Z)-octadecane-9-enoyl] oxypropyl] 2-(trimethyl ammonium) ethyl phosphate, 8-hydroxy-8-(3-octyl oxirane-2-yl) octanoic acid and N-((2S, 3R)-1, 3-dihydroxy octadecane-4-alkene-2-yl) acetamide. The four lipid metabolism markers (C44H80NO8P, C18H34O4, C20H39NO3 and C26H43NO5) screened by the invention are used for the prediction efficiency of MACE prediction, the sensitivity of the four lipid metabolism markers reaches 0.889, the specificity of the four lipid metabolism markers is 0.894, the AUC of the four lipid metabolism markers is 0.921, and the four lipid metabolism markers have high sensitivity and high specificity.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine, and in particular to a marker for predicting MACE risk in patients with acute ST-segment elevation myocardial infarction after PCI. Background Art

[0002] Risk prediction systems for major adverse cardiovascular events (MACE) in patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI) still have significant limitations. Existing technologies primarily rely on single biomarkers or traditional scoring models, and their predictive efficacy and clinical application face multiple bottlenecks.

[0003] The GRACE score can predict the risk of death or myocardial infarction within 1 year, but its parameters do not incorporate key pathophysiological parameters such as coronary microcirculatory status. The SYNTAX score accurately assesses the anatomy of complex lesions but ignores the dynamic changes of biomarkers. Furthermore, existing models are mostly based on data from European and American populations and are poorly calibrated in Asian populations. Inflammatory markers such as the neutrophil-to-lymphocyte ratio (NLR), while positively correlated with MACE risk, significantly decrease their specificity in the presence of concurrent infection or tumor, leading to an increased false-positive rate. The limitations of traditional risk scoring models are becoming increasingly apparent. At the biomarker level, CK-MB, a traditional marker of myocardial injury, has been gradually replaced by troponin. Its diagnostic sensitivity is relatively low and it is susceptible to procedural factors in the short period after PCI. For example, nonspecific elevations of CK-MB due to mechanical injury during coronary intervention make it difficult to accurately distinguish true myocardial necrosis from iatrogenic injury, resulting in reduced accuracy in MACE prediction. Currently, commonly used markers of myocardial injury in clinical practice include troponin and high-sensitivity troponin. While troponin has high sensitivity, it lacks specificity. High-sensitivity cardiac troponin (hs-cTn) is highly sensitive to myocardial injury, but its independent prediction area under the curve (AUC) for MACE is 0.65-0.72 and is susceptible to confounding by non-cardiac factors such as renal dysfunction. Furthermore, BNP and its precursor, NT-proBNP, primarily reflect changes in ventricular load and pressure and can predict the risk of heart failure and mortality, but their specificity is poor and is susceptible to individual differences such as age, renal function, and body mass index. BNP has limited predictive value for early MACE, particularly in the short postoperative period. The correlation between its dynamic changes and actual cardiovascular events remains unclear, limiting its clinical application. In recent years, with the deepening of research into the pathophysiological mechanisms of myocardial infarction, a number of novel biomarkers have gained increasing attention, including inflammatory markers (high-sensitivity C-reactive protein (hs-CRP) and interleukin-6 (IL-6), oxidative stress markers (malondialdehyde (MDA), and markers of the coagulation and fibrinolysis system (D-dimer and fibrinogen). These markers can reflect the pathophysiological process after STEMI, but their sensitivity, specificity, and predictive accuracy are still controversial and limited.

[0004] In addition, single biochemical markers are unlikely to effectively meet the clinical needs for predicting MACE risk after PCI. Current research trends favor the development of multi-marker combined prediction models to comprehensively assess patients' post-PCI risk and more accurately predict their long-term cardiovascular event risk. Summary of the Invention

[0005] In response to the deficiencies of the existing technology, the present invention proposes a marker for predicting the risk of MACE in patients with acute ST-segment elevation myocardial infarction after PCI.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The first aspect of the present invention relates to the use of reagents for detecting lipid metabolism markers in the preparation of a kit for predicting the risk of major adverse cardiovascular events after PCI in patients with acute ST-segment elevation myocardial infarction. The reagents for detecting lipid metabolism markers include: one or more of the following detection reagents: glycodeoxycholic acid, [(2R)-3-[(6Z,9Z,12Z)-octadeca-6,9,12-trienoyl]oxy-2-[(Z)-octadeca-9-enoyl]oxypropyl] 2-(trimethylammonium) ethyl phosphate, 8-hydroxy-8-(3-octyloxirane-2-yl)octanoic acid, and N-((2S,3R)-1,3-dihydroxyoctadec-4-ene-2-yl)acetamide.

[0008] Optionally, the reagent further comprises a detection reagent for amino-terminal pro-brain natriuretic peptide.

[0009] Optionally, the detection is based on QExactive TM Plus combined quadrupole Orbitrap TM Mass spectrometer.

[0010] Optionally, the object of detection is a serum sample from a patient.

[0011] The second aspect of the present invention relates to a kit for predicting the risk of major adverse cardiovascular events after PCI in patients with acute ST-segment elevation myocardial infarction, comprising one or more of the following detection reagents: glycodeoxycholic acid, [(2R)-3-[(6Z,9Z,12Z)-octadeca-6,9,12-trienoyl]oxy-2-[(Z)-octadeca-9-enoyl]oxypropyl]2-(trimethylammonium)ethyl phosphate, 8-hydroxy-8-(3-octyloxirane-2-yl)octanoic acid, and N-((2S,3R)-1,3-dihydroxyoctadec-4-ene-2-yl)acetamide.

[0012] The third aspect of the present invention relates to a system for predicting the risk of major adverse cardiovascular events in patients with acute ST-segment elevation myocardial infarction after PCI, comprising:

[0013] A sample collection module, used for collecting serum samples from patients;

[0014] A sample detection module is used to detect the content of lipid metabolism markers in serum samples;

[0015] and,a data processing module, which processes the test results and predicts risks;

[0016] Wherein, the lipid metabolism markers include: one or more of the detection reagents of glycodeoxycholic acid, [(2R)-3-[(6Z,9Z,12Z)-octadeca-6,9,12-trienoyl]oxy-2-[(Z)-octadeca-9-enoyl]oxypropyl]2-(trimethylammonium)ethyl phosphate, 8-hydroxy-8-(3-octyloxirane-2-yl)octanoic acid, and N-((2S,3R)-1,3-dihydroxyoctadec-4-ene-2-yl)acetamide.

[0017] Optionally, the sample detection module includes QExactive TM Plus combined quadrupole Orbitrap TM Mass spectrometer and mass spectrometry detection related reagents.

[0018] Beneficial effects of the present invention:

[0019] This study used a nested case-control design, with MACE occurring within six months after PCI in patients with acute ST-segment elevation myocardial infarction (STEMI) as the outcome. Liquid chromatography-mass spectrometry (UPLC-MS) was used to analyze the lipid metabolism profile of serum samples from STEMI patients before PCI to explore the association between lipid metabolites and MACE after PCI in STEMI patients. Four lipid metabolism markers (C 44 H 80 NO8P、C 18 H 34 O4、C 20 H 39 NO3 and C 26 H 43 NO5) showed high sensitivity and specificity in predicting MACE, with a sensitivity of 0.889, a specificity of 0.894, and an AUC of 0.921. These lipid metabolites reflect the role of lipid metabolism in various stages of the MACE-related process, enabling accurate assessment of the risk of postoperative cardiovascular events in STEMI patients. This provides a strong basis for early clinical identification of high-risk patients and optimization of individualized treatment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further described below with reference to the accompanying drawings.

[0021] Figure 1 This is a flow chart of the metabolic marker screening in the examples of this application;

[0022] Figure 2 This is the total ion current diagram of lipidomics in the examples of this application;

[0023] Figure 3This is a differential analysis of preoperative serum metabolites between the postoperative MACE group and the non-MACE group in STEMI patients in the examples of this application;

[0024] Figure 4 The predictive efficacy of the difference in preoperative serum metabolites between the postoperative MACE group and the non-MACE group in STEMI patients in the examples of this application;

[0025] Figure 5 The ROC curves for the prediction of MACE efficacy of the four lipid metabolites in the examples of this application are shown;

[0026] Figure 6 The ROC curve for predicting the MACE efficacy of the combination of the four lipid metabolites in the examples of this application is shown;

[0027] Figure 7 The global permutation importance for predicting MACE efficacy of the four lipid metabolites in the examples of this application;

[0028] Figure 8 is the average absolute SHAP value of the four lipid metabolites in the examples of this application for predicting MACE efficacy;

[0029] Figure 9 The partial dependence curves for predicting the MACE efficacy of the four lipid metabolites in the examples of this application are shown;

[0030] Figure 10 The figure shows the ROC curves of the six clinical markers in the examples of this application for predicting the efficacy of MACE. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0032] In the embodiments of the present invention, the biological samples used were sourced from: 83 patients with confirmed STEMI were recruited from the hospital according to the diagnostic criteria of the "Guidelines for the Diagnosis and Treatment of Acute ST-segment Elevation Myocardial Infarction (2019)", and general demographic characteristics and clinical biochemical indicators were collected. General demographic characteristics include gender, age, BMI (kg / m 2), systolic blood pressure (mmHg), diastolic blood pressure (mmHg), smoking, and drinking. The general clinical data of all enrolled patients were collected through the medical record system, including admission diagnosis, body mass index (BMI), past medical history (history of myocardial infarction, PCI, heart failure, hypertension, diabetes, hyperlipidemia, atrial fibrillation, stroke, and chronic kidney disease), preoperative medication history, and postoperative medication (mainly based on discharge medication, and the medication must be used for at least one week). Preoperative serum samples were collected. After percutaneous coronary intervention (PCI), patients were followed up for 6 months. MACE events usually include cardiovascular and cerebrovascular death, stroke, recurrent myocardial infarction, and re-hospitalization due to heart failure.

[0033] In various embodiments of the present invention, the equipment used includes: a high-resolution Q-Exactive combined quadrupole-Orbitrap liquid chromatography-mass spectrometry system (Thermo Scientific, USA); an ACQUITY UPLC BEH C18 chromatographic column (1.7 μm, 2.15 mm Column) (Waters, USA); an Acquity UPLC CSH C18 VanGuard pre-column (5×2.1 mm; 1.7 μm) (Waters, USA); a Milli-Q Advantage A10 ultrapure water system (Millipore, USA); KQ-500B ultrasonic cleaner (Kunshan Ultrasonic Instrument Co., Ltd., Kunshan); 5417R low-temperature high-speed centrifuge (Eppendorf, Germany); SORVALL large-capacity floor-standing centrifuge (Beckman, USA); Vortex-Genie2 vortex mixer (Scientific Industries, USA); ultraspeed refrigerated centrifuge (Beckman, USA); BSA124S-CW 1 / 10,000 electronic analytical balance (Sartorius, Germany). Methanol and acetonitrile (HPLC grade, Merck, Germany); formic acid, methyl tert-butyl ether (MBTE), and ethyl acetate (HPLC grade, ROE Scientific, USA); ammonium formate (HPLC grade, ROE Scientific, USA), sodium chloride, sodium citrate, and disodium hydrogen citrate (analytical grade, Huadong Medicine Company); 0.22 μm organic phase filter (Typre, Nanjing); brown injection vials (Ronghua, Nanjing); and inner liner (Ronghua, Nanjing).

[0034] After collecting the above serum sample, the pretreatment method may include the following steps:

[0035] (1) Pretreatment of serum samples

[0036] Liquid-liquid extraction was performed using methyl tert-butyl ether (MTBE) as the extraction solvent. Acetonitrile (ACN) and MTBE (3:5, v / v) were added to the serum to degrade proteins and separate the metabolites. After separation, lipid compounds were present in the upper, less dense organic solvent layer.

[0037] The specific method is as follows:

[0038] (1) Take out the collected serum samples and thaw them at 4℃. Vortex and mix them for 10s. Take 200mL of serum samples and place them in a 15mL centrifuge tube. The subsequent operations are carried out in an ice box. Take an equal volume of 20mL of each batch sample as a mixed quality control sample (QC). After vortexing and mixing, take 600mL and evenly distribute them in three 15mL centrifuge tubes (parallel samples). The QC samples are used to balance the chromatography-mass spectrometry system and evaluate the stability of the system.

[0039] (2) Add 600 mL of ACN pre-cooled at -20°C and oscillate for 10 seconds. Then add 1 mL of pre-cooled MTBE solution and vortex for 10 minutes. Ultrasonic extraction was performed in an ice-water bath for 10 minutes, and the mixture was allowed to stand at -20°C for 1 hour. Centrifuge for 15 minutes (13,000 rpm, 4°C), and all the supernatant was placed in a new 15 mL centrifuge tube for nitrogen blowing.

[0040] (3) After nitrogen flushing, the sample was re-dissolved in acetonitrile / isopropanol / water (v / v / v, 5:3:2) and vortexed for 10 min. Centrifuged for 15 min (13,000 rpm, 4°C), 150 mL of the supernatant was placed in an LC-MS injection vial with an inner liner and stored at -80°C until analysis.

[0041] (2) Serum lipid panel testing

[0042] (1) Ultra-high performance liquid chromatography conditions

[0043] The sample was separated on a Waters Acquity UPLC CSH C18 column (100 × 2.1 mm; 1.7 μm) and an Acquity UPLC CSH C18 VanGuard pre-column (5 × 2.1 mm; 1.7 μm). The column temperature was maintained at 55°C and the flow rate was 0.28 mL / min.

[0044] The positive ion mobile phase consisted of: (A) acetonitrile:water (60:40, v / v) containing ammonium formate (10 mM) and formic acid (0.1%); (B) isopropanol:acetonitrile (90:10, v / v) containing ammonium formate (10 mM) and formic acid (0.1%). The negative ion mobile phase consisted of: (A) acetonitrile:water (60:40, v / v) containing ammonium formate (10 mM); (B) isopropanol:acetonitrile (90:10, v / v) containing ammonium formate (10 mM). Elution conditions were as follows: 15% B at 0 min; 30% B at 0-2 min; 48% B at 2-2.5 min; 82% B at 2.5-11 min; 99% B at 11-13.5 min; 99% B at 13.5-17.5 min; 15% B at 17.5-17.6 min; and 15% B at 17.6-22 min.

[0045] (2) Mass spectrometry parameter acquisition conditions

[0046] The Q Exactive MS instrument was operated in electrospray (ESI) mode with the following parameters: mass range: 120–1800 m / z; sheath gas flow rate: 60; auxiliary gas flow rate: 25; scan gas flow rate: 2; spray voltage (kV): 3.6; capillary temperature: 300°C; S-lens RF level: 50; auxiliary gas heater temperature: 370°C. Full-scan MS parameters: resolution, 70,000 (maximum 140,000);

[0047] AGC target value, 1e6; maximum IT, 100 milliseconds; spectrum data type, center point. Data-related MS2 parameters: resolution, 17,500; AGC target, 1e5; maximum IT, 50ms; number of cycles, 10; TopN, 10; isolation window, 1.0 m / z; fixed first mass, 70.0 m / z; (N)CE / collision (N)CE: 20, 30, 40. Positive ion and negative ion scanning modes were used to acquire mass spectrometric signals for the samples, and ultrapure water was tested three times as a blank sample before each batch of analysis. To avoid systematic errors, samples were analyzed in a random order. At the same time, a QC sample was set for every 10 samples during the queue. The samples were placed in an autosampler at 4°C throughout the analysis process. The specific mass spectrometric parameter results are shown in. Figure 2 .

[0048] In some embodiments of the present invention, a method for analyzing the differences in general demographic characteristics and clinical indicators of an acute STEMI population, and screening a MACE risk prediction or diagnostic marker is disclosed, comprising the following steps:

[0049] Data were analyzed using SPSS 25.0 and R 4.0.4 statistical software. Continuous data were presented as mean plus or minus standard deviation if approximately normal; otherwise, median and quartiles were used. Count data were presented as frequency and composition ratio. Variables with more than 20% missing values ​​were excluded from the analysis. For included variables with a small number of extreme values ​​and missing values, two strategies were used: capping for extreme values ​​(values ​​outside 6 standard deviations), and imputation of the mean for a small number of missing values ​​(mostly <5%). Some experimental data below the detection limit were replaced with values ​​at half the detection limit. This approach minimized sample size while preserving the main results and conclusions. For quantitative variables with a normal or approximately normal distribution, mean comparisons were performed using the t-test. For variables with non-normal distributions, nonparametric tests (Mann-Whitney U test) were used. Count data were compared using Fisher's exact test or chi-square test. Given the small sample size, variables with a p < 0.2 were included in subsequent regression analyses. Stepwise regression was used to obtain a parsimonious, stable, and interpretable model.

[0050] (1) General demographic characteristics of the MACE group and the non-MACE group

[0051] Table 1 Comparison of general demographic characteristics between the MACE group and the non-MACE group

[0052]

[0053] Table 1 shows the comparison of general demographic characteristics of patients in the non-MACE group (n=47) and the MACE group (n=36). The results showed that none of the differences reached the statistical significance level (P<0.05).

[0054] (2) Analysis of differences in preoperative clinical indicators between the MACE group and the non-MACE group

[0055] Table 2 Differences in serum myocardial markers before surgery between MACE group and non-MACE group

[0056]

[0057] A comparison of myocardial markers between patients in the non-MACE group (n=47) and the MACE group (n=36) was presented. NT-proBNP was significantly higher in the MACE group than in the non-MACE group [96.05 (29.58, 395.32) vs. 42.00 (15.00, 158.70), P=0.045], suggesting a possible association with MACE. Similarly, lactate dehydrogenase (LDH) levels were significantly elevated in the MACE group [336.20 (207.98, 800.75) vs. 227.00 (180.05, 391.50), P=0.044]. Furthermore, α-hydroxybutyrate dehydrogenase (α-HBDH) levels were also higher in the MACE group (P=0.055), but this did not reach statistical significance. Other indicators, such as hs-cTnI, MYO, creatine kinase, creatine kinase isoenzymes, and aspartate aminotransferase, showed no statistically significant differences between the two groups (P>0.05). Overall, NT-proBNP and LDH may have some value in predicting MACE, but further research is needed to verify this.

[0058] Table 3 Differences in preoperative blood biochemistry and blood routine indicators between the MACE group and the non-MACE group

[0059]

[0060]

[0061]

[0062] Table 3 shows that regarding liver function indicators, the median and interquartile range of total bilirubin, direct bilirubin, and indirect bilirubin did not show significant differences (P>0.05). Urea and creatinine levels also did not differ significantly between the two groups, indicating that renal function in both groups was relatively stable. Regarding hematological indicators, there were no significant differences in white blood cell count, neutrophil percentage, absolute monocyte count, lymphocyte percentage, and absolute lymphocyte count (P>0.05). Only prothrombin activity showed a significant difference between the two groups (106.05±12.95 vs. 99.31±12.06, P=0.018).

[0063] (II) Analysis of serum differential metabolites in patients with acute ST-segment elevation myocardial infarction before PCI

[0064] Data analysis was performed using SIMCA-P 14.0 and RStudio. The measurement data were expressed as mean ± standard deviation. If the comparison between the two groups met normality and homogeneity of variance (P<0.05), two independent sample t-tests were used to compare the differences between the two groups. Otherwise, if normality was met but homogeneity of variance was not met, the Wilcoxon rank sum test was used. SIMCA-P14.0 was used to analyze metabolomics data in different models for multivariate analysis. The preoperative serum metabolomics data of each group (SQ1 for the MACE group and SQ0 for the non-MACE group) were preprocessed with unit variance scaling (UV Scaling) and mean centering. After preprocessing, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used for model analysis, respectively. R 2 and Q 2 Used to evaluate the quality of the model, indicating the fitness and predictive ability of the model respectively.

[0065] The limma package in R software was used to analyze differential metabolites between SQ0 and SQ1 patients. Statistically significant metabolites were selected based on the following criteria: P < 0.05 and FC > 1.5 / < 0.67. Pathway enrichment analysis of the differential metabolites was performed using the Pathway Analysi module of the Metaboanalyst website. Heatmap and hierarchical cluster analysis were performed using the pheatmap package. Logistic regression was also used to calculate the association between each metabolite and MACE outcomes in STEMI patients.

[0066] Based on the identified MACE-related metabolites, the random forest algorithm (rfe function) was first used to screen the best metabolites, and then the receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of the model.

[0067] (1) Difference analysis of preoperative serum metabolites between the MACE group and the non-MACE group

[0068] Serum metabolomics analysis was performed on 83 serum samples collected from STEMI patients before PCI. Metabolomics analysis revealed 737 metabolites.

[0069] Non-targeted metabolomics analysis was performed on 83 serum samples. The PLS-DA score graph showed that there was a significant difference between the SQ1 group and the SQ2 group. 2 X=0.425,R 2 Y=0.379,Q 2 Y=0.059(see Figure 2A). Among the detected metabolites, 374 differential metabolites (DM) were screened out based on FC values ​​and P values. Among them, 275 metabolites were increased in the SQ1 group, and the remaining 99 metabolites were decreased (see Figure 2 B). KEGG enrichment pathway results showed that the related pathways of differential metabolites mainly included unsaturated fatty acid biosynthesis, fatty acid biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis and linoleic acid (see Figure 2 C).

[0070] (2) Predictive efficacy of differential metabolites in serum between the MACE group and the non-MACE group before surgery

[0071] In order to obtain differential metabolites closely related to STEMI for predicting the occurrence of MACE, 361 metabolites were first extracted from the intersection of differential metabolites and significantly correlated metabolites obtained by logistic regression analysis (see Figure 3 A).

[0072] Subsequently, the 361 variables were screened using a random forest algorithm for bootstrap sampling. When the number of retained metabolites was 10, the model accuracy was the highest (see Figure 3 B).

[0073] Table 4 10 different substances screened

[0074]

[0075] Next, the 10 candidate metabolites (C 17 H 19 NO3, C 13 H 10 O3、C 44 H 80 NO8P、C 18 H 34 O4、C 20 H 39 NO3, C 26 H 43 NO5, C6H6O6S, C5H9NO4, NP-012768, C 11 H 19 NO9, C 20 H 34 O2) were searched. Among them, C 17 H 19 NO3, C6H6O6S and NP-012768 are exogenous substances, C 13 H 10 O3 and C 20 H 34O2 is a drug component, while C5H9NO4 is a polar substance and is not suitable for evaluation by this lipidomics method. Therefore, the above 6 metabolites were excluded. The remaining 4 metabolites (C 44 H 80 NO8P、C 18 H 34 O4、C 20 H 39 NO3 and C 26 H 43 NO5) as a candidate metabolite, and the detailed information of the substance is shown in Table 5.

[0076] Table 5 Characteristics of preoperative serum metabolic markers for prediction of postoperative MACE in STEM II patients

[0077]

[0078] Based on the four markers screened in the above embodiments, other embodiments of the present invention disclose the diagnosis of the four markers and the evaluation of the diagnostic efficacy.

[0079] like Figure 5 As shown in Figure 2, the GBM (Gradient Boosting Machine) algorithm was used to construct the prediction model. The hyperparameters were optimized through 10-fold cross-validation. Finally, the learning rate (shrinkage = 0.1), tree depth (interaction.depth = 2) and 50 trees (n.trees) were selected as the GBM prediction model parameters. The ROC curve was used to evaluate the predictive efficacy of the four metabolites screened above for MACE. 26 H 43 The AUC value of NO5 was 0.802( Figure 5 A), C 18 H 34 The AUC value of O4 was 0.861( Figure 5 B), C 20 H 39 The AUC value of NO3 was 0.789( Figure 5 C),C 44 H 80 The AUC value of NO8P was 0.798 ( Figure 5 D).

[0080] The above results indicate that by individually predicting risk using the four selected metabolites, superior diagnostic results can be obtained compared to existing technologies. In some embodiments, the four metabolites can be used in combination to further enhance diagnostic efficacy.

[0081] Specifically, based on the results of the previous random forest model, this example further constructed a combined prediction index system based on the above four lipid metabolism markers to evaluate the predictive efficacy of the risk of major adverse cardiovascular events (MACE) after PCI in STEMI patients. Figure 6 As shown in the ROC curve analysis results, the area under the curve (AUC) of the four metabolite combination model reached 0.921 (95% CI

[0082] 0.876-0.962), with good predictive efficiency (sensitivity was 0.889 and specificity was 0.894).

[0083] This embodiment further analyzes the contribution of each indicator in the joint prediction model. Figure 8 、 Figure 9 The global permutation importance and mean absolute SHAP value are shown in the figure, and the results consistently reveal that C 26 H 43 NO5 plays the most important role in the model: after disrupting its concentration, the model AUC decreases the most ( Figure 7 ), and its average SHAP value also ranks first ( Figure 8 ), indicating that it has the highest sensitivity and contribution to the prediction of MACE risk after PCI. The other three metabolic markers C 44 H 80 NO8P、C 18 H 34 O4 and C 20 H 39 NO3, the three are relatively similar in terms of substitution importance and SHAP contribution, indicating that the lipid metabolism pathways involved are also indispensable in risk judgment.

[0084] Figure 9 The partial dependence curves of each metabolite shown in A–D further illustrate their concentration-dependent relationship with MACE risk. 26 H 43 NO5 showed a monotonically positive correlation. The higher the concentration, the more significantly the predicted risk increased and tended to a plateau, reflecting the preoperative C 26 H 43 The linear relationship between the continuous increase of NO5 level and the risk of postoperative adverse cardiovascular events; C 18 H 34 O4 and C 44 H 80 NO8P showed a monotonically negative correlation with the risk of MACE. The lower the concentration, the more significant the increase in the risk of MACE, suggesting that it is a protective factor for MACE. 20 H 39Fluctuations in the mid-range of NO3 concentrations can reflect significant changes in MACE risk, suggesting that it may be involved in key metabolic processes related to the occurrence of MACE. 26 H 43 NO5, C 44 H 80 NO8P、C 18 H 34 O4 and C 20 H 39 NO3 combined with markers can effectively improve the prediction accuracy of postoperative MACE risk.

[0085] Based on the clinical indicators of this study population, the predictive value of existing clinical biomarkers for MACE was analyzed. Figure 10 The results showed that the AUC value of myocardial marker MYO was 0.575 ( Figure 10 A), the AUC value of NT-proBNP was 0.756 ( Figure 10 B), the AUC value of hs-CTnl was 0.684 ( Figure 10 C); the AUC value of traditional myocardial enzymes: CK-MB was 0.694 ( Figure 10 D), the AUC value of CK was 0.681 ( Figure 10 E), the AUC value of LDH was 0.757 ( Figure 10 F) The predictive value of these clinical markers is lower than that of the lipid metabolism markers and marker combinations screened above.

[0086] In terms of the evaluation indicators of predictive efficacy, the metabolite joint prediction model constructed in the present invention showed better balance, indicating that the multi-biomarker combination strategy based on lipid metabolomics showed higher predictive value in MACE risk prediction compared with the current clinical routine detection indicators, and provided a new potential biomarker combination for optimizing individualized risk management after PCI surgery.

[0087] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0088] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. Use of a reagent for detecting lipid metabolism markers in the preparation of a kit for predicting the risk of major adverse cardiovascular events in patients with acute ST-segment elevation myocardial infarction after PCI, characterized in that: The reagents for detecting lipid metabolism markers include: one or more of the following detection reagents: glycodeoxycholic acid, [(2R)-3-[(6Z,9Z,12Z)-octadeca-6,9,12-trienoyl]oxy-2-[(Z)-octadeca-9-enoyl]oxypropyl]2-(trimethylammonium)ethyl phosphate, 8-hydroxy-8-(3-octyloxirane-2-yl)octanoic acid, and N-((2S,3R)-1,3-dihydroxyoctadec-4-ene-2-yl)acetamide.

2. Use of the reagent for detecting lipid metabolism markers according to claim 1 in preparing a kit for predicting the risk of major adverse cardiovascular events in patients with acute ST-segment elevation myocardial infarction after PCI, characterized in that: The detection is based on liquid chromatography and mass spectrometry equipment.

3. Use of the reagent for detecting lipid metabolism markers according to claim 1 in preparing a kit for predicting the risk of major adverse cardiovascular events in patients with acute ST-segment elevation myocardial infarction after PCI, characterized in that: The object of the detection is a serum sample of a patient.

4. A kit for predicting the risk of major adverse cardiovascular events in patients with acute ST-segment elevation myocardial infarction after PCI, characterized in that: include: One or more of the detection reagents of glycodeoxycholic acid, [(2R)-3-[(6Z,9Z,12Z)-octadec-6,9,12-trienoyl]oxy-2-[(Z)-octadec-9-enoyl]oxypropyl]2-(trimethylammonium)ethyl phosphate, 8-hydroxy-8-(3-octyloxirane-2-yl)octanoic acid, and N-((2S,3R)-1,3-dihydroxyoctadec-4-en-2-yl)acetamide.

5. A system for predicting the risk of major adverse cardiovascular events in patients with acute ST-segment elevation myocardial infarction after PCI, characterized in that: include: A sample collection module, used for collecting serum samples from patients; A sample detection module is used to detect the content of lipid metabolism markers in serum samples; and,a data processing module, which processes the test results and predicts risks; Wherein, the lipid metabolism markers include: one or more of the detection reagents of glycodeoxycholic acid, [(2R)-3-[(6Z,9Z,12Z)-octadeca-6,9,12-trienoyl]oxy-2-[(Z)-octadeca-9-enoyl]oxypropyl]2-(trimethylammonium)ethyl phosphate, 8-hydroxy-8-(3-octyloxirane-2-yl)octanoic acid, and N-((2S,3R)-1,3-dihydroxyoctadec-4-ene-2-yl)acetamide.

6. The system for predicting the risk of major adverse cardiovascular events in patients with acute ST-segment elevation myocardial infarction after PCI according to claim 5, characterized in that: The sample detection module includes liquid chromatography and mass spectrometry equipment.

7. The system for predicting the risk of major adverse cardiovascular events in patients with acute ST-segment elevation myocardial infarction after PCI according to claim 5, characterized in that: The data processing module includes a GBM algorithm to build a prediction model.

8. The system for predicting the risk of major adverse cardiovascular events in patients with acute ST-segment elevation myocardial infarction after PCI according to claim 7, characterized in that: In the prediction model constructed by the GBM algorithm, the learning rate shrinkage=0.1 and the tree depth interaction.depth=2.

Images