Recent progress in metabolomic analysis of acute coronary syndrome: a narrative review

Introduction

Acute coronary syndrome (ACS) is a clinical syndrome caused by acute myocardial ischemia, and is one of the leading causes of death worldwide (1). The main pathogenesis of ACS is the sudden rupture and superficial erosion of atherosclerotic plaque in the process of occurrence and development, which causes thrombosis and eventually leads to insufficient blood supply or necrosis of myocardium (2-4). ACS subtypes include unstable angina (UA), non-ST-segment elevation myocardial infarction (NSTEMI) and ST-segment elevation myocardial infarction (STEMI) (5). Currently, the diagnosis of ACS requires a combination of clinical and electrocardiographic (ECG) signals, and myocardial injury markers are necessary to distinguish the various subtypes of ACS. However, not only may biomarkers such as troponin be increased in other diseases, but the identification of different subtypes also requires ongoing detection of ECG and biomarkers of myocardial damage in conjunction with clinical manifestations (6). There is undoubtedly an urgent need for more low-cost but highly sensitive and specific biomarkers.

With the continuous advancement of science and technology in the field of systems biology, after genomics and proteomics, metabolomics has also entered everyone’s field of vision (7). Metabolomics can qualitatively and quantitatively analyze all small molecule metabolites in an organism, providing an avenue for studying the relative relationships between physiological and pathological changes. Since dynamic perturbations of the genome, transcriptome, and proteome can all be reflected by changes in an organism’s metabolites, metabolites can efficiently link genotype to phenotype (8). For this reason, metabolomics can provide information about organisms under normal or pathological conditions, and reflect changes in organisms in response to metabolites stimulated by external stimuli (9). Metabolomics has been widely used to discover novel diagnostic and prognostic biomarkers and more therapeutic targets to improve disease outcomes (10).

In previous studies, researchers have also reviewed the metabolomic-related studies of ACS. In 2019, Pouralijan Amiri et al. (10) provided a comprehensive review of the pathogenesis of ACS, metabolomics techniques, and several studies on the application of metabolomics for detecting ACS. Their work highlights that metabolomics is a robust method for characterizing metabolic disturbances associated with various cardiovascular diseases. In 2021 and 2023, researchers augmented the existing technical methodologies in metabolomics, elucidated the experimental protocols for detecting diseases through metabolomics, and synthesized findings from previously identified altered metabolic pathways associated with ACS (1,11). This paper focuses more on the potential diagnostic and prognostic biomarkers and their diagnostic and prognostic abilities found in previous studies based on metabolomics, and summarizes the types they belong to, so as to find the metabolic pathways that are most prone to change when diseases occur. We present this article in accordance with the Narrative Review reporting checklist (available at https://cdt.amegroups.com/article/view/10.21037/cdt-24-431/rc).

Methods

This study is a narrative review aimed at identifying and searching current literature on studies related to the metabolomics of ACS. A combination of “acute coronary syndrome”, “unstable angina”, “NSTEMI, STEMI” and “metabolomics” was used to search PubMed literature in English from January 2014 to December 2024. We included articles that identified potential diagnostic and prognostic markers for ACS and their subtypes, as well as articles that identified mechanisms of drug treatment for the disease through metabolomics. The main objective of our review was to summarize the potential diagnostic and prognostic markers identified by metabolomics over the last decade and to explore the changes in metabolic pathways that most commonly occur in ACS. Table 1 summarizes the search strategies used in our review, the full electronic search strategy can be seen in Table S1.

Techniques applied to metabolomics analysis

Currently, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry are the two most common analytical tools used in metabolomics studies (12). NMR is a pioneering platform for metabolomics that combines non-destructiveness, unbiasedness, high stability and reproducibility. Its main advantages are the small sample size required and simple pre-treatment, which can provide accurate molecular structures of compounds (13). However, the greatest problem is that it is not as sensitive as mass spectrometry and is prone to signal overlap, leading to inaccurate results (14). Mass spectrometry is generally combined with gas chromatography (GC-MS) and liquid chromatography (LC-MS), the overall sensitivity of mass spectrometry is higher than that of NMR, and the application of LC-MS is more widespread (15). LC-MS detection of metabolites is not limited by difficult volatility and thermal instability, and sample preparation is simpler than GC-MS as no derivatization is needed (8). GC-MS can be used for the detection of volatile or non-polar metabolites but sample preparation is complex. And it is very difficult to detect non-volatile or thermally unstable, easily degradable substances (12). The main advantages and disadvantages of NMR, LC-MS and GC-MS techniques are summarized in Table 2. In general, it is difficult to detect all metabolites with a single detection technique alone, so an increasing number of groups are now combining NMR and MS, using their complementary functions to improve the quality of detection, and to identify more metabolic compounds (16).

Application of metabolomics to discover potential biomarkers of ACS

The development of disease is often associated with genetic or phenotypic changes in the organism, so we have been able to diagnose and predict the occurrence of disease through changes in genes, proteins and metabolites. In contrast, most researchers believe that metabolites are better suited for clinical biomarker development and research (17). The number of biomarkers has been increasing in recent years, and over the past decade, many researchers have conducted extensive research on diagnostic and prognostic biomarkers associated with ACS. A comprehensive list of metabolomics studies on ACS focusing on diagnostic biomarkers and prognostic biomarkers is presented in Tables 3,4.

Metabolomics-based diagnostic biomarker analysis of ACS

Currently, the diagnosis of ACS relies on comprehensive analysis of clinical manifestations, electrocardiograms, and myocardial biomarkers of myocardial injury (74). Although the continuous advancement of technology has greatly improved the accuracy of diagnosis, there are still insufficient and certain limitations. Although markers of myocardial injury have good sensitivity, the specificity of creatine kinase-MB (CK-MB) is not strong, and an increase in troponin reflects only myocardial injury (75). If there is no clinical evidence that the injury is related to coronary ischemia, the myocardial injury may be caused by other causes (76). Metabolomics studies can detect a large number of metabolites, and metabolites can also be effective new biomarkers.

For ACS, there have been a number of articles in which valuable diagnostic biomarkers have been identified. In 2017, Yin et al. (18) used a non-targeted metabolomics approach (LC-MS) to identify novel biomarkers from plasma samples from 20 ACS patients and 20 non-ACS patients. Researchers found that phospholipid, glycolysis, and amino acid metabolism are altered in ACS patients compared with non-ACS patients. In 2021, Zhong et al. (19) applied a non-targeted LC-MS approach to plasma metabolomic analysis of 284 ACS patients and 130 healthy controls (HCs) to identify 28 metabolites that could be used to differentiate between the two groups. The receiver operating characteristic (ROC) curve showed that the combination of two metabolites, phosphatidylethanolamine lyso (16:0), and LPC (20:4), into one panel had an area under the curve (AUC) value of 0.905. In the same year, applying ultra-performance liquid chromatography coupled with the time of flight (UPLC-Q-TOF/MS) technique, Song et al. (20) examined serum samples from 45 ACS patients and 29 HCs and identified 46 differentially abundant metabolites. ROC curve analysis revealed that the three metabolites were used as diagnostic biomarkers with AUC values of 0.92–0.93. The diagnostic model constructed with metabolic biomarkers combined with cTNI had a high AUC value of 0.96. In 2024, Shibata et al. (24) also analyzed the metabolomic characteristics of 18 patients with ACS and 24 age-matched healthy volunteers by UPLC-Q-TOF/MS. A multiple logistic regression model for the diagnosis of ACS was established, in which the area under the ROC curve value of the lysine, isocitrate and tryptophan metabolite panel for distinguishing ACS patients from HCs was 1.00.

UA is the earliest type of disease process in ACS. In 2016, Ameta et al. (57) used 1H NMR to analyze serum samples from 65 patients with UA and 62 HCs. It was found that the group of five metabolites, valine, alanine, glutamine, inosine, and adenine, was sufficient to achieve the maximum area under the ROC curve (AUC =0.99). In 2019, Wang et al. (58) examined serum samples of UA, unstable angina-diabetes mellitus (UA-DM), and HCs using the UPLC-Q-TOF/MS technique. First, 27 differential metabolites were identified by comparison of UA and HC. Pathway analysis showed that arginine and proline metabolism, glycerophospholipid metabolism, and purine metabolism were affected in patients with UA. Then, the authors applied metabolomics to detect UA and UA-DM metabolites and identified 22 differential metabolites. ROC analysis showed that six had high specificity and sensitivity for distinguishing UA from UA-DM, with AUC values above 0.85. Pathway analysis suggested that tryptophan metabolism is a key metabolic pathway in patients with UA combined with DM. In 2024, Hao et al. (64) used metabolomics to analyze serum samples from 33 UA, 38 myocardial infarction (MI) and 24 normal controls. The results revealed significant differences between MI and UA in xylene, hydroxycaproic acid, butylbenzene sulfonamide, octanetriol, phosphocholine and medronic acid. Pathway analysis showed that it was related to myocardial hypertrophy, Wnt signaling and fatty acid oxidation.

AMI is a serious threat to human health, because of persistent ischemia and hypoxia in the coronary arteries, which can cause myocardial necrosis and even death. In 2016, Fan et al. (25) applied the liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-Q-TOF/MS) technique to 1,086 blood samples [116 normal coronary artery (NCA), 276 non-obstructive coronary atherosclerosis (NOCA), 63 stable angina (SA), 307 UA, 324 acute myocardial infarction (AMI)] from center 1 for metabolomics. The results of the training set were validated by applying 933 blood samples from center 1 and 71, 68, and 166 blood samples from center 2, center 3, and center 4. The authors focused on the effects of NOCA versus NCA on plaque formation, SA versus NOCA on plaque growth, UA versus SA on the transition from stable to unstable coronary arteries, and AMI versus UA. A total of 89 differential metabolites were identified. Phospholipid catabolism was reduced, amino acid metabolism was increased, short-chain acylcarnitines were increased, the tricarboxylic acid cycle was reduced, and primary bile acid biosynthesis was reduced. The AUC values for the 12 metabolomics-specific biomarker-based experimental groups ranged from 0.938 to 0.996, providing 89.2% to 96.0% predictive values in the test phase and 85.3% to 96.4% predictive values in the 3-center outer set. In 2021, Aa et al. (26) applied GC-MS and LC-MS techniques to identify patients with AMI after the onset of chest pain versus patients with other cardiogenic chest pain (UA, myocarditis, heart valve disease) and combined the results of both methods and found that deoxyuridine (dU), homoserine and methionine could be potential biomarkers for AMI (AUC >0.91).

In the clinical setting, AMI is further classified into STEMI and NSTEMI based on the changes in the ST segment of the electrocardiogram. In 2015, Naz et al. (48) analyzed serum samples from 16 NSTEMI patients and 16 STEMI patients using CE-MS and identified 32 significant metabolites. Then, 13 carnitine-derived compounds and 13 amino acids were targeted and quantified in the serum of 20 STEMI patients and 28 NSTEMI patients using the HILIC-MS technique, and the c02-carnitine levels differed significantly between the two groups with an AUC of 0.85. Because it is crucial to examine left main coronary artery disease (LMCAD) before the occurrence of STEMI or sudden infarction death, in 2018, Huang et al. (49) examined serum samples including 44 STEMI patients (22 consecutive LMCAD and 22 non-LMCAD) using UPLC-MS technology. Fourteen metabolites were significantly different between LMCAD and non-LMCAD. ROC curve analysis showed that 9-cis-retinoic acid (9cRA) was the most important biomarker distinguishing the two groups with an AUC value of 0.888. Subsequently, the authors established a panel of 10 metabolites including 9cRA, dehydrophytosphingosine, 1H-indole-3-carbaldehyde, and 7 variants of lysophosphatidylcholine (LysoPC) with the largest AUC (0.933). In 2023, Luo et al. (55) applied a combination of metabolomics and OCT to detect the difference between plaque rupture (PR) and plaque erosion (PE). The results showed that docosahexaenoic acid (DHA), salicylic acid and proline were identified in both tests. ROC analysis showed that the areas under the curve of these metabolites in the training samples were 0.81, 0.70 and 0.67 respectively. The AUC values of 0.75, 0.73 and 0.74 were verified in the test samples.

Metabolomics-based prognostic biomarker analysis of ACS

The discovery of more novel biomarkers for early prognostic assessment of disease is essential to provide patients with more appropriate treatment strategies and further management (77). The 2023 ESC Management Guidelines emphasize the role of serial measurements of high sensitivity cardiac troponin (hs-cTn) and consideration of changes in plasma brain natriuretic peptide (BNP) or N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentrations in the prognosis of ACS (78). One study found a substantial increase in the maximum chance of death in patients with elevated cardiac troponin T (cTnT) and cardiac troponin I (cTnI) (79). Another study showed that the NT-proBNP level at 6 weeks after ACS diagnosis was an independent predictor of future adverse outcomes. However, the predictive power of these biomarkers currently varies widely across disease endpoints, and the exploration of new prognostic biomarkers with high sensitivity and specificity is urgently needed (80).

Among the retrieved AMI studies, in 2019, Vignoli et al. (65) performed metabolomic analysis of serum samples from 80 surviving patients and 40 deceased patients, and for each serum sample, a nuclear Overhauser effect spectrum (NOESY) one-dimensional 1H-NMR spectrum was obtained. For each patient, a NOESY RF score was derived from the obtained NOESY spectrum, with the “RF risk score” indicating the extent to which the serum metabolomic profile appeared to be similar to that of one of the deceased patients. The metabolic profiles of 80 patients who survived and 40 patients who died were classified using the radio frequency (RF) classifier. The performance of the RF risk scores was compared with the actual results. The ROC analysis revealed an AUC of 0.859 for the NOESY spectrum, and the validation set (106 patients who died and 752 who survived) using the optimized NOESY RF risk score derived from the training set, the AUC value was 0.801. In 2022, Liu et al. (66) collected serum samples from 79 patients with AMI with cardiovascular disease (AMICVD), 28 of whom died within 1 year as the AMI with cardiovascular death (AMICVDD) group. A biomarker panel identifying three metabolites namely TMAO, OAG, and histidine was found to discriminate AMICVDD from AMICVD, and the AUC of this biomarker panel was 0.85 in the validation phase, which was the same as that in the discovery phase.

For NSTEMI and STEMI, there are also a number of previous articles that have identified valuable prognostic biomarkers. In Surendran et al.’s study (68) in 2019, a total of 108 plasma samples from 27 patients with STEMI were examined to identify changes in plasma metabolites during human ischemia/reperfusion (I/R) injury and to find potential plasma biomarkers to assess the extent of myocardial injury after PPCI. The researchers identified a panel of pentanoic acids, carnitine linoleate and choline 1-linoleate glycerophosphate as plasma biomarkers of severity with AUC values of 0.86. In 2021, Liu et al. (69) applied UPLC-MS technology to perform a metabolomic analysis of serum collected from 48 survivors (SSTEMI) and 48 non-survivors (DSTEMI). Twenty-six differential metabolites were identified, seven of which were strongly associated with the occurrence and different outcomes of STEMI. ROC curve analysis revealed multivariate linear support vector machine (SVM) algorithm and generalized estimating equation (GEE) model analysis of the panel consisting of seven metabolites with AUC values of 0.998 and 0.981, respectively.

For prognostic markers of ACS, many other researchers have applied cohort analysis. In 2016, Laaksonen et al. (70) studied the prognostic value of plasma ceramide (Cer) as a marker of cardiovascular death (CV death) in three independent coronary artery disease (CAD) cohorts. This finding was ultimately validated in a prospective Norwegian cohort study of patients with stable CAD, which demonstrated that ceramides, especially when used in proportion, are significantly associated with cardiovascular death. In the Corogene, SPUM-ACS, and BECAC studies, different plasma ceramide ratios were found to be important predictors of cardiovascular death in patients with stable coronary heart disease (CHD) and ACS. In 2023, Kraler et al. (71) recruited data from 2,619 ACS patients, follow-up for 30 days and 1 year, respectively, and mortality endpoints to observe whether changes in low-density lipoprotein (LDL) electronegativity are associated with adverse outcomes in patients with ACS. The results showed that changes in LDL electronegativity were associated with 30-day and 1-year all-cause mortality. It has replaced several risk factors that predict 1-year death.

Application of metabolomics to discover drugs for the prevention and treatment of ACS

ACS is most often caused by obstruction and requires the establishment of acute in-perfusion of blood flow. Currently, reperfusion is mostly achieved clinically with thrombolytic agents or percutaneous coronary intervention (PCI). Despite advances in treatment, the treatment and prognosis of ACS remain unsatisfactory, so new therapeutic approaches and medication targets are urgently needed (81). A list of the therapeutic effects and mechanisms of drugs for ACS are presented in Table 5.

Metabolomics-based study of ACS prevention through herbal medicine influenced metabolites

For the treatment of ACS, in addition to some therapeutic drugs, there are also some traditional Chinese medicines that play preventive roles in this disease. Xin-Ke-Su (XKS) is a patented drug that is extensively used in the treatment of ACS. In 2014, Liu et al. (84) established a myocardial infarction model after pretreating mice with different levels of XKS, and metabolomic analysis of serum samples from each group showed that XKS could protect rats from metabolic disorders through lipid pathways, amino acid metabolism, and purine metabolism thus protecting the cardiovascular system.

Using HPLC-Q-TOF-MS/MS technique, Wu et al. (85) examined body fluid samples from different groups of rats, which were rat models of acute myocardial infarction established by ligation of the left anterior descending branch of the coronary artery. This study demonstrated that Sukei Jiuxin Pills (SJP) achieve cardiovascular protection by regulating the metabolism of amino acids and fatty acids in the body. In the same year, Wu’s team applied the same technique to study the action and analysis mechanism of Shexiang Baoxin Pill (SBP), and the results revealed that SBP protects the heart by regulating amino acid, lipid, and energy metabolism (86).

Metabolomics-based study to treat ACS with drug-influenced gut microbial metabolites

According to previous studies, the microbiota and derived microbial compounds of the gut might play a positive role in human metabolism and may be relevant to the pathogenesis of some common metabolic diseases (89). Meanwhile, metabolites associated with gut microbes make important contributions to cardiovascular disease.

Hu et al. (82) applied 16SrRNA and untargeted UPLC-Q-TOF/MS techniques to samples from ACS patients with and without statin therapy to explore the mechanisms by which statins affect key gut microbes and metabolites for cardiovascular protection in ACS patients, and reported that statins modulate bacteria in the gut, and that gut bacteria modulate metabolites such as fatty acids and acrolein lipids to regulate disease severity.

On the other hand, Lam et al. (83) established a rat model in which multiple antibiotics were acted on rats after which feces, blood heart tissue were collected for testing, and the researchers found that antibiotic treatment reduced intestinal microbial metabolites of the amino acids phenylalanine, tryptophan and tyrosine were associated with a reduced severity of myocardial infarction.

Metabolomics-based metabolic pathway analysis of the major differential metabolites of ACS

Metabolic pathway analysis of ACS diagnostic biomarkers

We reviewed 50 articles applying metabolomics to detect diagnostic biomarkers of ACS in the last decade, of which 304 metabolites have been applied as diagnostic biomarkers. These metabolites can be categorized into ten groups, including amino acids, lipids, organic acids, hormones, carbohydrates, hydrocarbons, vitamins, purines, and amines. Lipids accounted for 29% of the metabolites, and can be subdivided into fatty acids (13%), glycerophospholipids (12%), and sphingolipids (4%). Amino acids account for 18% of which peptides account for approximately 4%. Organic acids accounted for 11%. Hormones account for approximately 10% of which steroids account for 6%. The percentages of other types of metabolites are shown in Figure 1. Table 6 shows the metabolites found in ACS-related articles according to metabolite categorization ranked by the number of times they were mentioned in different articles.

Figure 1 Type and number of acute coronary syndrome diagnostic biomarkers identified in the last decade.

There are 42 amino acid metabolites of which tryptophan (21%), glutamine (17%), isoleucine (14%), and alanine (17%) have been mentioned several times in several articles. Amino acid metabolites mainly regulate amino acid-related metabolic pathways in the body. Some also regulate the tricarboxylic acid cycle in the body. Tryptophan is associated with UA mainly regulates the tryptophan metabolic pathway in the body, which plays an important role in protein synthesis, endothelium-derived blood pressure control in stroke vessels, and microvascular inflammatory response (31). However, in addition to UA glutamine, isoleucine, and alanine have been associated with STEMI. Patients with AMI have increased serum levels of alanine, glutamine, histidine, valine, and isoleucine, suggesting that the tricarboxylic acid cycle (TCA) cycle is facilitated and may be supplied by these amino acids. Elevated levels of amino acids also indicate disruption of lipoprotein degradation and energy metabolism in patients with acute myocardial infarction (33). A total of 11 metabolites that can be categorized as peptides, including carnosine, histidinyl-valine, glutathione, and phenylalanyl-phenylalanine are mentioned in the article. Among them, phenylalanyl-phenylalanine and phenylalanyl-tryptophan are associated with the lipid peroxidation pathway of AMI (40).

Lipids can be classified as fatty acids, glycerophospholipids, or sphingolipids. Among fatty acids linoleic acid (10%) and arachidonic acid (10%) are mentioned in several articles. Both metabolites are associated with acute infarction and are involved in the fatty acid metabolic pathway of the body. The level of unsaturated fatty acids in the blood of AMI patients suggests that the large amount of lipids accumulating in the blood vessels may increase the risk of atherosclerosis (30). Some pharmacologic studies suggest that omega-3 polyunsaturated fatty acid supplementation during rehabilitation after AMI may improve lipid metabolism and endothelial function by modulating the arachidonic acid metabolic status (88). In addition, LysoPC has been suggested in several articles to be associated with ACS, and LysoPC activates a wide range of cell types in the vascular system, accelerating the evolution and progression of atherosclerosis by inducing cell proliferation, and increasing lymphocyte. Plaque formation and thrombus rupture, which are also related mainly to the ratio of LysoPC to lysophosphatidylethanolamine (LysoPE) in the blood (36). The most mentioned metabolite of sphingosine is dehydrophytosphingosine (17%), which can be used as a diagnostic and prognostic marker for AMI. It attenuates vascular inflammation or oxidative stress through different mechanisms, such as the activation of G protein-coupled receptors or peroxisome proliferator-activated receptors (49).

Thirty metabolites were classified as organic acids, and lactate (12%) and 3-hydroxybutyrate (9%) were identified several times in previous articles. As better diagnostic biomarkers for AMI and STEMI, elevated levels of 3-hydroxybutyrate and lactate consistently indicate impaired fatty acid oxidation, tissue hypoxia, and increased anaerobic glycolysis, and elevated plasma lactate levels in patients with acute myocardial infarction can vary according to the extent of injury (60). The carbohydrate group includes 18 metabolites, of which inosine, Myo-inositol, glucose have been identified in several articles, inositol, and glucose have been associated with AMI and can be used as diagnostic biomarkers of AMI. Serum glucose levels are higher in AMI patients than in control subjects, suggesting accelerated gluconeogenesis and altered anaerobic glycolysis. In addition, the serum levels of inosine, a purine degradation product involved in anaerobic glycolysis, are increased in AMI patients, further confirming the observation of altered anaerobic glycolysis and energy metabolism (33).

Metabolic pathway analysis of ACS prognostic biomarkers

A total of 9 prognostic articles were mentioned in the article, and a total of 25 metabolites were found to be prognostic biomarkers of ACS. These prognostic metabolites can also be categorized into eight groups, including amino acids, lipid, organic acids, carbohydrates, hydrocarbons, and amines. Lipids accounted for 20% of the metabolites. Amino acids accounted for 28%, and organic acids accounted for 12%. The percentages of other types of metabolites are shown in Figure 2. Table 7 shows the metabolites found in ACS-related articles. Among the prognostic markers, the amino acid metabolites with the best proportions were mentioned, and histidine (29%) was mentioned in several articles. Previous study has found that histidine metabolism is closely related to the death outcome of AMI (66). L-histidine (HIS) can be synthesized by histidine decarboxylase. HIS also regulates the expression of histidases, produces uric acid, converts it to imidazoline propionate and hydrolyzes it to formiminoglutamate (FIGLU). The glutamate produced by FIGLU can be used to synthesize glutamine, which is eventually released into the blood. Researchers have reported elevated levels of histidine, glutamate, and glutamine in the event of eventual death from AMI (66).

Figure 2 Type and number of acute coronary syndrome prognostic biomarkers identified in the last decade.

The carbohydrate group included 3 prognostic metabolites, and two AMI prognosis-related articles identified mannitol as a potential prognostic marker. Mannose (67%) is responsible for maintaining the steady state of the blood and plays a central role in the glycosylation of lipoproteins involved in the development and progression of atherosclerosis. N-glycans are upregulated in a pro-inflammatory milieu and are found on the surface of endothelial cells during the early stages of atherosclerotic plaque formation. Authors reported that patients at high risk for AMI relatively high levels of mannose, suggesting that mannose binds relatively little to mannose-binding lectin (MBL), which reduces the activation of the lecithin pathway, one of the three modes of complement system activation (67).

Ceramide, a sphingomyelin metabolite among lipid metabolites, was mentioned in three articles as a prognostic marker for ACS. Researchers have reported that ceramides can promote a number of central atherosclerotic processes, including lipoprotein aggregation and uptake, inflammation, superoxide ion production, and apoptosis (70). High ceramide levels have also been found to be associated with cardiac cell death in a mouse model of myocardial infarction. In addition, ceramides can cause vascular dysfunction by inactivating endothelial nitrogen monoxide (NO) synthase (72).

Conclusions and future expectations

In conclusion, ACS is a common CAD, and although researchers have conducted metabolomic studies on ACS and successfully identified potential biomarkers for diagnosis, prognosis, and some pharmacological treatments, current studies have various shortcomings that need improvement.

In current studies on ACS, the maximum sample size collected in experiments is around 1,000 cases, but very few can reach this number, and most experiments have a sample size of a few hundred cases or even a few dozen cases. Therefore, in future studies, we can expand the sample size and collect multicenter samples, which will make the results more convincing. In addition, most of the current studies collected validation groups to verify the specificity and sensitivity of diagnostic and prognostic biomarkers, and several studies on AMI performed targeted metabolomics assays, however, they also targeted only one class of metabolites and did not target previously identified metabolite biomarkers. Therefore, in future studies, targeted metabolomics should be used more over to quantify previously identified valuable metabolites and to provide a more powerful account of the previous studies. Finally, we can also improve the accuracy of metabolite discovery by integrating metabolic data generated by different metabolic technologies, and we can also combine genomics and proteomics technologies to use multi-omics to better understand the regulatory networks among substances, to further understand the biological pathways and mechanisms of diseases.

It is believed that with the continuous advancement of technology and the efforts of millions of researchers, more accurate and specific markers will continue to emerge, leading to greater breakthroughs in the diagnosis and prevention of ACS, thus improving the current status of ACS diagnosis and treatment.

Acknowledgments

We express our gratitude to Wei Sun for his valuable assistance in the selection and writing of this article.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://cdt.amegroups.com/article/view/10.21037/cdt-24-431/rc

Peer Review File: Available at https://cdt.amegroups.com/article/view/10.21037/cdt-24-431/prf

Funding: This work was supported by the Department of Science and Technology of Jilin Province (No. 20200404193YY).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://cdt.amegroups.com/article/view/10.21037/cdt-24-431/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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