ABSTRACT

   Metabolic disorders play a crucial role in the occurrence of acute
   myocardial infarction (AMI). The objective of this research was to
   elucidate the characteristic metabolic profile of AMI and provide
   potential biomarkers for AMI. This study employed a targeted
   metabolomics approach utilizing the Ultra Performance Liquid
   Chromatography Tandem Mass Spectrometry (UPLC‐MS/MS) system to identify
   both hydrophilic and hydrophobic metabolites present in plasma samples.
   Among 1498 detected metabolites, 78 were the most significantly
   expressed in the AMI group. Functional synergy analysis showed
   prominent enrichment in the pathways of steroid hormone biosynthesis,
   biosynthesis of unsaturated fatty acids, bile secretion, and ABC
   transporters. The metabolites 2‐Hydroxy‐6‐Aminopurine,
   17α‐Hydroxyprogesterone, and S‐(methyl) glutathione have been
   identified as potential metabolic biomarkers linked to the pathogenesis
   of AMI. The diagnostic model that integrates these three metabolites
   exhibited exceptional performance in both the discovery and validation
   cohorts, attaining an area under the curve (AUC) value greater than
   0.9. In addition, based on the follow‐up data, we also found that the
   three metabolites were potential predictive biomarkers for poor
   prognosis of AMI. This study delineated the characteristic metabolic
   profile of AMI and assessed the value of metabolic molecules in the
   diagnosis and prognosis of AMI. This may provide insights for
   understanding the AMI occurrence and progression.

   Keywords: AMI, diagnosis, metabolomics, prognosis, UPLC‐MS/MS
     __________________________________________________________________

   This study detected 1498 metabolites using untargeted metabolomics in
   discovery and validation sets. Multivariate analysis revealed
   differences between healthy controls, AMI, and UA groups, identifying
   AMI‐specific metabolites. Enrichment analysis explored their biological
   significance. Machine learning techniques like random forest and neural
   networks identified high‐potential biomarkers, validated in an
   independent set for reliability.

   graphic file with name MCO2-6-e70303-g002.jpg

1. Introduction

   Cardiovascular diseases (CVDs) continue to represent the predominant
   health threat and persist as the leading cause of mortality on a global
   scale [[32]1]. Acute myocardial infarction (AMI) is the most common and
   fatal CVD caused by necrosis of the cardiac myocytes due to prolonged
   ischemia and hypoxia, characterized by high incidence, with more than 7
   million newly diagnosed cases per year [[33]2]. The World Health
   Organization (WHO) reported that AMIs account for approximately 31% of
   the total deaths globally. In China, the number of hospital admissions
   for AMI has increased more than fourfold, and the age of onset tends to
   be younger [[34]3]. In addition, with the improvement of economic
   conditions and the intensification of aging, the prevalence of
   hypertension and hyperlipidemia is steadily rising in China [[35]4].
   This further leads to an increase in the incidence of AMI. Once AMI is
   established, it progresses rapidly and results in high mortality rates
   [[36]5]. Research has demonstrated that early identification, expedited
   diagnostic procedures, and timely intervention can significantly reduce
   mortality rates and enhance the prognosis for patients with AMI.
   Therefore, it is crucial to identify novel biomarkers that can help
   diagnose AMI and improve AMI prognoses.

   The clinical presentations of AMI patients, including chest tightness
   and/or persistent chest pain accompanied by dyspnea, are similar to
   that of unstable angina (UA) patients, and UA is often classified as a
   precursor of AMI. Currently, the identification of AMI primarily
   depends on the presence of irregularities in the electrocardiogram and
   increased concentrations of cardiac biomarkers [[37]6], and the latter
   indicates irreversible damage to the cardiac myocytes, which requires
   urgent identification of novel diagnostic biomarkers of AMI.
   Metabolomics is regarded as an emerging discipline that belongs to the
   downstream system of biology and can reflect the metabolic processes of
   organisms [[38]7]. In recent years, small molecule metabolites have
   shown excellent clinical application potential in the diagnosis of
   diseases [[39]8]. Metabolic abnormalities are also the most prominent
   features of CVDs. Utilizing a high‐resolution metabolomics platform,
   Adnan Khan and colleagues discovered that tryptophan, carnitine,
   L‐homocysteine sulfinic acid, and cysteic acid serve as promising
   biomarkers for differentiating AMI patients from healthy controls (HC)
   [[40]9]. Utilizing ultra‐performance liquid chromatography coupled with
   a quadruple time‐of‐flight mass spectrometry platform, Park et al.
   discerned notable disparities in lipid metabolism profiles between
   patients diagnosed with AMI and those suffering from UA [[41]10]. It
   has been shown that small molecule metabolites may not only serve as
   biomarkers for disease diagnosis and prognosis but can also act as
   active drivers in biological processes and may participate in the
   regulation of the transcriptome and metabolome levels [[42]8, [43]11].
   Therefore, outlining the distinct metabolic profiles may aid in
   elucidating the underlying mechanisms of pathogenesis and in
   discovering novel therapeutic targets for AMI.

   Nevertheless, the metabolic profiles associated with AMI and UA remain
   inadequately understood. In the present investigation, we conducted a
   comprehensive metabolomic analysis utilizing plasma samples obtained
   from 120 patients diagnosed with CVDs of various etiologies alongside
   30 HC subjects. This analysis was executed employing an Ultra
   Performance Liquid Chromatography Tandem Mass Spectrometry (UPLC‐MS/MS)
   platform in conjunction with targeted detection methodologies. In
   addition, multiple machine learning algorithms were applied to identify
   the potential metabolome biomarkers associated with AMI prognosis,
   which were externally validated. There might be new insights into the
   pathogenesis and diagnosis of AMI based on this observation.

2. Results

2.1. Identification of Full Spectrum Metabolites

   The study included a total of 150 participants, who were divided into
   two distinct groups: the discovery cohort and the validation cohort
   (Table [44]1). A thorough analysis identified a total of 1498
   metabolites, comprising both hydrophilic and hydrophobic varieties,
   through the application of the UPLC‐MS/MS platform in conjunction with
   advanced targeted lipid quantification methodologies. The detailed
   design process of this study is shown in the flow diagram
   (Figure [45]1).

TABLE 1.

   The clinical data and laboratory test results of all patients.
   Discovery set (n = 90) Validation set (n = 60)
   Characteristic HC (n = 30) UA (n = 30) AMI (n = 30) UA (n = 30) AMI (n
   = 30)
   Age ≤ 60 14 13 14 13 15
   > 60 16 17 16 17 15
   Sex Male 21 21 23 21 23
   Female 9 9 7 9 7
   CK‐MB 15.35 (13.43, 18.48) 54.45 (23.77, 194.47) 13.75 (11.03, 17.88)
   37.5 (24.6, 160.8)
   TC 4.88 ± 0.86 4.85 ± 1.05 4.57 ± 1.06 5.13 ± 1.25
   TG 1.77 ± 1.31 1.34 ± 0.84 1.48 ± 0.66 1.57 ± 1.14
   HDL‐C 1.23 ± 0.29 1.27 ± 0.73 1.15 ± 0.21 1.13 ± 0.23
   LDL‐C 2.88 ± 0.79 2.94 ± 0.9 2.65 ± 0.81 3.1 ± 1.04
   [46]Open in a new tab

   Abbreviations: AMI, acute myocardial infarction; CK‐MB, creatine
   kinase‐MB; HC, healthy control; HDL‐C, high‐density lipoprotein
   cholesterol; LDL‐C, low‐density lipoprotein cholesterol; TC, total
   cholesterol; TG, triglycerides; UA, unstable angina.

FIGURE 1.

   FIGURE 1
   [47]Open in a new tab

   The workflow chart for this study.

2.2. Identification of Metabolites With Differential Expression

   Principle component analysis (PCA) showed the separation between AMI,
   UA, and HC groups (Figure [48]2A), which reveals the considerable
   changes in the metabolome profile during AMI development. The score
   plot of the orthogonal partial least squares discriminant analysis
   (OPLS‐DA), as depicted in Figure [49]2B, demonstrates a significant
   distinction in metabolite profiles between the AMI group and the HC
   group. The permutations test evaluated the OPLS‐DA model's robustness
   by calculating R ^2 and Q ^2 values. With Q ^2 = 0.932 and R ^2
   Y = 0.986, the model is reliable and stable, effectively identifying
   differential metabolites (Figure [50]2C). The identification of
   differential metabolites between the two groups was carried out
   utilizing the variable importance in projection (VIP) value, fold
   change, and p value, with the results subsequently illustrated in a
   volcano plot. In the AMI group, 175 metabolites were upregulated, and
   186 were downregulated (Figure [51]2D). We also compared metabolite
   differences between the UA and AMI groups. PCA showed obvious spectral
   separation between the two groups and a high degree of consistency
   within groups (Figure [52]2E). Similarly, the OPLS‐DA plot showed that
   there were significantly different metabolic characteristics between
   the UA group and the AMI group (Figure [53]2F). The permutation
   analysis validated the robustness and reliability of the OPLS‐DA model,
   yielding R ^2 Y and Q ^2 values of 0.972 and 0.832, respectively
   (Figure [54]2G). A comprehensive evaluation revealed that a total of
   188 metabolites exhibited differential expression across the groups,
   with 157 metabolites demonstrating an increase and 31 showing a
   decrease in the AMI cohort (Figure [55]2H). We analyzed and compared
   the differing metabolite profiles between the UA and HC groups.
   Employing PCA and OPLS‐DA, we identified a distinct separation in the
   metabolic profiles of these two cohorts (Figure [56]S1A, B). The
   volcano plot analysis indicated that 86 metabolites were upregulated,
   whereas 209 metabolites were downregulated in the UA group relative to
   the HC group (Figure [57]S1C). Moreover, the KEGG enrichment analysis
   revealed that these metabolites exhibiting significant differences were
   predominantly concentrated within various metabolic pathways, such as
   thermogenesis, nucleotide metabolism, and regulation of the actin
   cytoskeleton (Figure [58]S1D). Subsequently, we conducted a
   comprehensive analysis by integrating the metabolic data from the three
   groups. PCA revealed a significant differentiation between the AMI and
   UA groups compared to the HC group, although some overlap was observed
   in the metabolic profiles of the AMI and UA groups relative to the HC
   group (Figure [59]S2A). OPLS‐DA further demonstrated significant
   differentiation in the metabolic profiles among the three groups.
   Additionally, the credibility of the model was confirmed through a
   permutation test (Figure [60]S2B,C). An ANOVA test was employed to
   determine the primary differential metabolites among the three groups,
   with a significance threshold set at p < 0.05. Furthermore, KEGG
   enrichment analysis revealed that these differential metabolites were
   predominantly associated with pathways such as thermogenesis,
   pyrimidine metabolism, nucleotide metabolism, and glyoxylate and
   dicarboxylate metabolism, among others (Figure [61]S2D).

FIGURE 2.

   FIGURE 2
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   The serum metabolic profiles of the acute myocardial infarction (AMI)
   group, unstable angina (UA), and the healthy control (HC) group. (A)
   The PCA scores plot showed the separation trend between the AMI group
   and the HC group. (B) OPLS‐DA plot was used to characterize the
   differential metabolites between the AMI group and the HC group. (C) A
   permutation test was applied 200 times to assess the risk of
   over‐fitting for the OPLS‐DA model. (D) Volcano plot displaying the
   differences in metabolite concentrations between the AMI group and the
   HC group. (E) PCA scores plot showed the separation trend between the
   AMI group and the UA group. (F) OPLS‐DA plot was used to characterize
   the differential metabolites between the AMI group and the UA group.
   (G) A permutation test was applied 200 times to assess the risk of
   over‐fitting for the OPLS‐DA model. R ^2 X and R ^2 Y, respectively,
   represent the interpretation rate of the model to the X and Y matrices,
   and Q ^2 represents the prediction ability of the model. (H) Volcano
   plot displaying the differences in metabolite concentrations between
   the AMI group and the UA group.

2.3. Metabolic Pathways Enrichment Analysis

   The KEGG pathway enrichment analysis performed on the differential
   metabolites differentiating the HC group from the AMI group identified
   significant enrichments in pathways related to pathogenic Escherichia
   coli infection, glycerophospholipid metabolism, and autophagy processes
   (Figure [63]3A). Metabolite set enrichment analysis (MSEA) showed that
   sphingolipid metabolism, steroid hormone biosynthesis, pyrimidine
   metabolism, tryptophan metabolism, thiamine metabolism, biotin
   metabolism, and beta‐alanine metabolism were significantly enriched
   between the HC group and the AMI group (Figure [64]3B). KEGG analysis
   revealed that the metabolites exhibiting significant differences
   between the UA and the AMI group were primarily enriched in
   thermogenesis, biosynthesis of unsaturated fatty acids, cortisol
   synthesis and secretion, Cushing syndrome, fatty acid biosynthesis,
   steroid hormone biosynthesis, and ABC transporters (Figure [65]3C).
   MESA revealed notable differences between the UA and AMI groups in
   purine metabolism, steroid hormone biosynthesis, vitamin B6 metabolism,
   histidine metabolism, and phenylalanine metabolism pathways
   (Figure [66]3D).

FIGURE 3.

   FIGURE 3
   [67]Open in a new tab

   Enrichment analysis of differential metabolites. (A) Kyoto Encyclopedia
   of Genes and Genomes (KEGG) enrichment analysis of differential
   metabolites between acute myocardial infarction (AMI) and healthy
   control (HC). (B) Metabolic set enrichment analysis between AMI and HC.
   (C) KEGG enrichment analysis of differential metabolites between AMI
   and unstable angina (UA). (D) Metabolic set enrichment analysis between
   AMI and UA.

2.4. K‐Means Analysis

   We conducted K‐means clustering to examine metabolite trends across
   three groups, identifying five subclasses (Figure [68]4A). Subclasses
   2, 3, and 5 showed consistent trends, either increasing or decreasing,
   from the HC group to the UA group and then to the AMI group. Then, we
   performed KEGG enrichment analysis on the metabolites in these three
   classes to understand their potential functions. The metabolites in
   subclass 2 were mainly enriched in retrograde endocannabinoid
   signaling, the neurotrophy signaling pathway,
   glycosylphosphatidylinositol‐anchor biosynthesis, diabetic
   cardiomyopathy, and autophagy pathways (Figure [69]4B). Subclass 3
   metabolites were primarily enriched in actin cytoskeleton regulation,
   neuroactive ligand–receptor interaction, glycerophospholipid
   metabolism, and choline metabolism in cancer pathways (Figure [70]4C).
   Steroid hormone biosynthesis, Cushing syndrome, cortisol synthesis and
   secretion, biosynthesis of unsaturated fatty acids, ABC transporters,
   and other pathways were significantly enriched in subclass 5
   (Figure [71]4D).

FIGURE 4.

   FIGURE 4
   [72]Open in a new tab

   K‐means clustering and enrichment analysis. (A) The cluster analysis of
   differential metabolites was performed through K‐means clustering
   algorithms. (B) KEGG enrichment analysis of metabolites in Subclass 2.
   (C) KEGG enrichment analysis of metabolites in subclass 3. (D) KEGG
   enrichment analysis of metabolites in subclass 5. Acute myocardial
   infarction; AMI, unstable angina; UA, healthy control: HC.

2.5. Screening of Potential Biomarkers for AMI

   We used a Venn diagram analysis to identify metabolites unique to the
   AMI group. The results showed that 74 metabolites were identified as
   common differential metabolites of the HC group and the AMI group and
   the UA group and the AMI group, of which 64 metabolites were
   upregulated and 10 were downregulated (Figure [73]5A). KEGG enrichment
   pathway analysis showed that these common differential metabolites were
   mainly enriched in steroid hormone biosynthesis, Cushing syndrome,
   cortisol synthesis and secretion, bile secretion, and ABC transporters
   (Figure [74]5B). A random forest algorithm was applied to screen the
   key biomarkers for distinguishing between the UA group and the AMI
   group. The importance of predictors in the constructed regression trees
   was ranked, and the top 10 metabolites were S‐(Methyl) glutathione,
   17α‐Hydroxyprogesterone, 2‐Hydroxy‐6‐Aminopurine, 11,12‐EET, Traumatic
   acid, Carnitine C18:2‐OH, 2,4‐Dihydroxy‐6‐pentylbenzoic acid,
   Allocholic acid, L‐Theanine, and Lithocholic acid (Figure [75]5C).
   Metabolites with relative importance greater than 2 were considered as
   potential biomarkers for AMI, including S‐(Methyl) glutathione,
   17α‐Hydroxyprogesterone, and 2‐Hydroxy‐6‐Aminopurine. Serum creatine
   kinase‐MB (CK‐MB) enzyme is often used in clinical diagnosis of AMI.
   Total cholesterol (TC), triglyceride (TG), high‐density lipoprotein
   cholesterol (HDL‐C), and low‐density lipoprotein cholesterol (LDL‐C)
   are commonly used as risk factors for CVDs. In the present study, we
   found that the correlation between the three metabolites and commonly
   used clinical indicators was weak (Figure [76]5D). Subsequently, we
   defined minimum (0) and maximum values (1) based on the median
   expression level of the three metabolites in the discovery set, and the
   hidden layers’ number was 5 to build an artificial neural network (ANN)
   (Figure [77]5E).

FIGURE 5.

   FIGURE 5
   [78]Open in a new tab

   Screening and identification of serum biomarkers. (A) Venn diagram
   showing the common differential metabolites. (B) KEGG enrichment
   analysis of common differential metabolites. (C) Identification of the
   top 10 important metabolites in distinguishing between the acute
   myocardial infarction (AMI) group and the unstable angina (UA) group by
   random forest algorithm. (D) Pearson correlation analysis was used to
   evaluate the correlation between metabolites and traditional clinical
   indicators. (E) Visualization results of artificial neural network.

   Receiver operating characteristic (ROC) analysis indicated the model's
   strong diagnostic accuracy with an area under the curve (AUC) of 0.985
   (Figure [79]6A). Additionally, a logistic regression model for the
   three metabolites in the discovery cohort achieved an AUC of 0.982
   (Figure [80]6B). Subsequently, the prediction efficacy of these three
   metabolites was evaluated in the discovery set. ROC analysis indicated
   that 2‐Hydroxy‐6‐Aminopurine, 17α‐Hydroxyprogesterone, and S‐(Methyl)
   glutathione were promising biomarkers with AUC of 0.802, 0.872, and
   0.941, respectively (Figure [81]6C). In the validation set, we also
   established the ANN model and logistic regression model of the three
   biomarkers, with an AUC of 0.927 and 0.957, respectively
   (Figure [82]6D,E). Similarly, these three metabolic biomarkers
   (2‐hydroxy‐6‐aminopurine, 17α‐Hydroxyprogesterone, and S‐(methyl)
   glutathione) also showed good diagnostic efficacy in the validation
   set, with AUC of 0.828, 0.813, and 0.918, respectively (Figure [83]6F).
   To evaluate the potential of three metabolites as AMI diagnostic
   biomarkers, we merged the discovery and validation sets. The
   metabolites effectively distinguished between the AMI and UA groups,
   with the ANN model achieving an AUC of 0.953 and the logistic
   regression model an AUC of 0.957 (Figure [84]6G,H). The
   2‐Hydroxy‐6‐Aminopurine, 17α‐Hydroxyprogesterone, and S‐(Methyl)
   glutathione had strong robustness and stable prediction performance in
   the whole set, with AUC of 0.818, 0.844, and 0.929, respectively
   (Figure [85]6I).

FIGURE 6.

   FIGURE 6
   [86]Open in a new tab

   ROC analysis of diagnostic biomarkers. (A) ROC analysis of the
   artificial neural network model in the discovery set. (B) ROC analysis
   of the logistic regression model in the discovery set. (C) Diagnostic
   efficacy of the three metabolites in the discovery set. (D) ROC
   analysis of the artificial neural network model in the validation set.
   (E) ROC analysis of the logistic regression model in the validation
   set. (F) Diagnostic efficacy of the three metabolites in the validation
   set. (G) ROC analysis of the artificial neural network model in the
   whole set. (H) ROC analysis of the logistic regression model in the
   whole set. (I) Diagnostic efficacy of the three metabolites in the
   whole set.

2.6. Correlation Analysis Between Clinical Features and Metabolites

   To clarify the diagnostic value of the three metabolites in AMI, we
   conducted univariate and multivariate logistic regression analyses.
   Univariate analysis identified CK‐MB and the three metabolites as AMI
   risk factors in the discovery set (Table [87]2). Multivariate analysis
   showed these metabolites as independent AMI risk factors, separate from
   CK‐MB (Table [88]2). The validation set confirmed these metabolites as
   independent AMI risk factors through both analyses (Table [89]3).

TABLE 2.

   Univariate and multivariate logistic regression in the discovery set.
   Univariate analysis Multivariate analysis
   Characteristics OR (95% CI) p value OR (95% CI) p value
   CK‐MB 1.132 (1.040–1.233) 0.004 NS NS
   TC 0.970 (0.567–1.660) 0.913 NS NS
   TG 0.677 (0.404–1.137) 0.14 NS NS
   HDL‐C 1.156 (0.448–2.979) 0.765 NS NS
   LDL‐C 1.081 (0.589–1.986) 0.801 NS NS
   2‐Hydroxy‐6‐Aminopurine 1.260 (1.106–1.435) < 0.001 1.222 (1.055–1.416)
   0.008
   17α‐Hydroxyprogesterone 1.386 (1.172–1.639) < 0.001 1.306 (1.081–1.578)
   0.006
   S‐(Methyl)glutathione 1.983 (1.448–2.715) < 0.001 1.702 (1.224–2.367)
   0.002
   [90]Open in a new tab

   Abbreviations: CK‐MB, creatine kinase‐MB; HDL‐C, high‐density
   lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol;
   NS, no significance; OR, odds ratio; TC, total cholesterol; TG,
   triglycerides.

TABLE 3.

   Univariate and multivariate logistic regression in the validation set.
   Univariate analysis Multivariate analysis
   Characteristics OR (95% CI) p value OR (95% CI) p value
   CK‐MB 1.145 (1.049–1.249) 0.002 NS NS
   TC 1.546 (0.959–2.490) 0.074 NS NS
   TG 1.107 (0.632–1.939) 0.723 NS NS
   HDL‐C 0.677 (0.064–7.146) 0.746 NS NS
   LDL‐C 1.724 (0.951–3.128) 0.073 NS NS
   2‐Hydroxy‐6‐Aminopurine 1.339 (1.159–1.546) < 0.001 1.320 (1.112–1.567)
   0.002
   17α‐Hydroxyprogesterone 1.306 (1.107–1.540) 0.002 1.239 (1.040–1.475)
   0.016
   S‐(Methyl)glutathione 1.502 (1.235–1.827) < 0.001 1.286 (1.060–1.560)
   0.011
   [91]Open in a new tab

   Abbreviations: CK‐MB, creatine kinase‐MB; HDL‐C, high‐density
   lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol;
   NS, no significance; OR, odds ratio; TC, total cholesterol; TG,
   triglycerides.

   We analyzed the expression levels of three metabolites in the HC and
   AMI groups, finding significant upregulation in the AMI group in both
   the discovery and validation sets (Figure [92]7A,B). To assess their
   prognostic value, 60 AMI patients were followed for 18 months, with 21
   (35%) hospitalized for CVDs. ROC analysis indicated these metabolites
   could predict second hospitalizations in AMI patients, with AUCs of
   0.617, 0.559, and 0.706, respectively (Figure [93]7C). Univariate and
   multivariate logistic regression analyses indicate that S‐(Methyl)
   glutathione could be an independent prognostic factor, regardless of
   CK‐MB levels (Table [94]S1). Furthermore, we constructed a logistic
   regression model for the three metabolites, and the AUC value reached
   0.716 (Figure [95]7D).

FIGURE 7.

   FIGURE 7
   [96]Open in a new tab

   Expression and prognosis analysis of metabolites. (A) Expression of the
   three metabolites in the discovery set. (B) Expression of the three
   metabolites in the discovery set. The expression levels were log2 and
   Z‐score normalized. (C) Prognostic efficacy of the three metabolites in
   AMI. (D) ROC analysis of logistic regression model. ****p < 0.0001.

3. Discussion

   AMI has attracted widespread attention for its high incidence rate and
   mortality [[97]2]. Over 9 million people worldwide die annually from
   AMI, the leading cause of death for both genders. The rise of
   reperfusion therapy has improved AMI patient outcomes [[98]12].
   However, the optimal treatment period of AMI is within 6 h after onset.
   Early diagnosis is the core strategy to improve the treatment effect
   and prognosis of AMI [[99]13]. Metabolites have shown great potential
   as biomarkers in a variety of complex diseases [[100]14, [101]15].
   Metabolomics is an important tool for large‐scale detection of
   metabolites and also has great potential to elucidate disease
   initiation and progression [[102]8, [103]11, [104]16]. Previously,
   independent lipid metabolomics or nontargeted metabolomics have been
   used to explore the plasma metabolic profile of AMI [[105]9, [106]17,
   [107]18, [108]19, [109]20]. In this study, 1498 metabolites were
   identified based on the UPLC‐MS/MS platform and targeted full‐spectrum
   metabolomics. The combination of quantitative lipidomics and widely
   targeted metabolomics is more conducive to systematically depicting the
   characteristic metabolic spectrum of AMI. In the present study, PCA and
   OPLS‐DA analysis indicated that the metabolic profile of the AMI group
   was significantly different from that of the HC and UA groups.

   In this study, 361 significantly different metabolites were identified
   between the HC group and the AMI group. These differential metabolites
   covered many species, including amino acid and its metabolomics, bile
   acid, fatty acyl, hormones, and hormone‐related compounds. The changes
   in these metabolic pathways may be the potential risk factors for AMI,
   and monitoring the dynamic changes of related metabolites may be
   beneficial to the early prevention of AMI. In addition, we also
   compared the metabolic profiles between the UA group and the AMI group,
   and a total of 188 differential metabolites were identified. KEGG
   analysis revealed that purine metabolism, thermogenesis, fatty acid
   biosynthesis and biosynthesis, of unsaturated fatty acid pathways were
   significantly enriched. Changes in these pathways may also be related
   to the alteration of the ischemia and hypoxia microenvironment in
   patients with AMI [[110]21]. Ischemia could induce the utilization of
   stored energy, including energy supply by lipolysis and activation of
   purine metabolism [[111]22, [112]23, [113]24]. Furthermore, we also
   found that the ferroptosis pathway was enriched. Ferroptosis occurs due
   to the accumulation of lipid peroxides and eventually leads to
   iron‐dependent programmed cell death. Studies have shown that
   ferroptosis plays an important role in acute cardiac ischemic injury
   [[114]25, [115]26, [116]27]. In this study, arachidonic acid was
   significantly upregulated in the AMI group and was associated with the
   activation of the ferroptosis pathway. Our results suggested that
   ferroptosis was activated in AMI at the level of metabolic pathways.
   Furthermore, we also found that ABC transporters, bile secretion,
   steroid hormone biosynthesis, cortisol synthesis, and secretion were
   specifically enriched in the AMI group compared with the HC and UA
   groups. ABC transporters are widely distributed in the outer membrane
   of cells and participate in the transmembrane transport of a variety of
   endogenous substances and xenobiotics, including lipid molecules,
   intracellular hormones, and other small molecules, by mediating ATP
   hydrolysis [[117]28, [118]29]. Meanwhile, studies have reported that
   ABC transporters play a critical role in maintaining the balance of
   bile acid metabolism and cholesterol metabolism [[119]30, [120]31,
   [121]32, [122]33]. Previous studies also reported that aberrant
   expression of ABC transporter‐related protein is closely related to
   atherosclerosis, chronic inflammation, and other diseases [[123]34]. As
   we know, the occurrence of AMI is inseparable from the formation of
   atherosclerosis and the continuous stimulation of chronic inflammation.
   These results suggested that maintaining the stability of the ABC
   transporters metabolic pathway is essential for preventing AMI.

   The random forest algorithm identified 2‐hydro‐6‐aminopurine,
   17α‐hydroxyprogesterone, and S‐(methyl) glutathione as possible
   biomarkers for AMI. S‐(Methyl) glutathione, a derivative of amino acid
   metabolism, is mainly produced by the combination of halomethanes with
   glutathione under the catalysis of glutathione‐S‐transferase T1
   [[124]35]. The accumulation of halomethanes in the human body has been
   reported to be associated with the impairment of human immunity and the
   occurrence of cancer [[125]36, [126]37, [127]38]. Our data showed that
   S‐(Methyl) glutathione was significantly enriched in the AMI group,
   indicating that halomethanes may be enriched in patients with AMI.
   17α‐Hydroxyprogesterone is mainly produced in the adrenal cortex, and
   its accumulation in the body is primarily due to 21‐hydroxylase
   (CYP21A2) deficiency. In this study, the 21‐hydroxylase deficiency
   (CYP21) pathway was also significantly enriched in the AMI group.
   Studies have reported that the abnormal accumulation of
   17α‐Hydroxyprogesterone in the body could increase the risk of CVD
   [[128]39, [129]40]. However, the relationship between
   17α‐Hydroxyprogesterone level and incident risks in subjects with AMI
   has not been investigated. Our data revealed that
   17α‐Hydroxyprogesterone may be a potential biomarker for the diagnosis
   of AMI. 2‐hydroxy‐6‐aminopurine, also known as guanine, is one of the
   four constituent bases of nucleic acids. Guanine in the human body is
   low, but it plays an indispensable role in maintaining normal
   physiological function and normal metabolism. Guanine is especially
   prone to oxidative damage in vivo, producing oxidative guanine damage
   products, which can cause DNA damage [[130]41, [131]42]. However, there
   are few reports about the abnormal accumulation of guanine in the body
   and its role in the occurrence of AMI. Our results suggested that
   guanine may be a risk factor for AMI, and further investigation is
   needed to clarify its role. Pearson correlation analysis indicated no
   significant correlation between these three metabolites and clinical
   risk indicators. The three metabolites were found to be independent
   risk factors for AMI through logistic regression analyses, and ROC
   analysis confirmed their strong predictive ability in both the
   discovery and validation sets, with AUC values over 0.8. These results
   suggested that the three metabolites could complement traditional
   clinical indicators and may serve as independent risk factors. We
   subsequently built a diagnostic model employing ANN and logistic
   regression, which exhibited excellent predictive ability in both the
   discovery and validation cohorts, achieving an AUC greater than 0.9.
   Our findings indicate that the three metabolites, particularly
   S‐(Methyl) glutathione, could be useful for predicting AMI prognosis
   and the likelihood of a second admission. Nevertheless, in comparison
   to the widely accepted biomarkers BNP and NT‐proBNP, the predictive
   value of the three AMI biomarkers identified in this study for heart
   failure requires further validation through larger cohort studies.

   This study is subject to several limitations that warrant
   consideration. While this study incorporates both a discovery set and a
   validation set, further validation of the identified biomarkers in a
   larger cohort remains essential. Hypoxia in cardiomyocytes plays a
   crucial role in the pathogenesis of AMI, necessitating a deeper
   investigation into the relationship between the metabolic markers we
   identified and hypoxia. Moreover, the expression of these biomarkers in
   high‐risk populations and their implications for tertiary prevention
   warrant more comprehensive examination. The roles and potential
   mechanisms of these biomarkers in the process of infarctogenesis
   necessitate further validation through in vivo or in vitro
   experimentation.

   This study utilizes UPLC‐MS/MS in conjunction with targeted
   full‐spectrum metabolomics to characterize the metabolic profile
   associated with AMI. Our findings revealed significant changes in
   glycerophospholipid metabolism, thermogenesis, steroid hormone
   biosynthesis, bile secretion, and ABC transporters between AMI and
   UA/HC subjects. The identified metabolome biomarkers
   2‐hydroxy‐6‐aminopurine, 17α‐hydroxyprogesterone, and S‐(methyl)
   glutathione might be of great importance for the diagnosis and
   prognosis of AMI, identifying new potential targets for novel treatment
   approaches to decrease the high mortality rate of AMI.

4. Materials and Methods

4.1. Characteristics of Patients

   In this research, blood samples collected from AMI patients (n = 60),
   UA (n = 60), and healthy normal volunteers (HC, n = 30) were subjected
   to metabolome analysis and phenotyping (AMI = 30; UA = 30; HC = 30) to
   identify the potential biomarkers associated with AMI pathogenesis. The
   findings were externally validated using an independent cohort
   (AMI = 30; UA = 30). Subjects with inheritable genetic disorders,
   infectious diseases, cancer, severe mental illness, and severe organ
   diseases were excluded. In this study, blood was collected from all
   patients within 6 h of onset, and none of the patients had taken any
   operative treatment. All participants were recruited from Yuebei
   People's Hospital. The ethical committee of Yue Bei People's Hospital,
   which is affiliated with Shantou University Medical College located in
   Shaoguan, China, granted ethical approval for the study design and
   protocol (KY‐2021‐050). Patients and volunteers were informed, and
   written consent was obtained. The framework and methodology employed in
   this investigation adhered to the principles established by the
   Declaration of Helsinki. Inclusion Criteria: Individuals identified
   with AMI in accordance with recognized clinical protocols. Exclusion
   Criteria: Individuals presenting with significant hypertension,
   respiratory issues, cardiac arrhythmias, hepatic and renal impairments,
   neoplastic conditions, and psychiatric disorders, as well as pregnant
   and breastfeeding women, in addition to those diagnosed with infectious
   and congenital diseases, were excluded from the study. Furthermore, we
   conducted a follow‐up study involving 60 patients with AMI over a
   duration of 18 months. The follow‐up endpoint was defined as
   hospitalization due to CVD during this period or follow‐up to
   18 months.

4.2. Sample Processing and Metabolite Extraction

   All samples were transported to the laboratory in coolers promptly
   following their collection, and plasma separation was carried out under
   sterile conditions. The plasma was sub‐packaged to the Eppendorf tube
   (200 µL per tube) and then stored in a −80°C refrigerator
   (TDE60086FV‐ULTS, Thermo) until further use. Samples were removed from
   the freezer and then transferred immediately into the icebox to
   facilitate gradual thawing, and all subsequent operations were
   conducted on ice. Plasma samples were completely thawed, followed by a
   vortex for 10 s to ensure adequate mixing. 50 µL of supernatant was
   absorbed from the mixed sample and transferred to a sterile and clean
   centrifuge tube. Subsequently, hydrophilic and hydrophobic metabolites
   were extracted according to the corresponding requirements. The
   extraction method for hydrophilic metabolites was as follows: (1) to
   each sample, 300 µL of extraction solution containing 20% acetonitrile
   (34851, Merck) methanol internal standard was directly transferred to
   50 µL of plasma and then vortexed for 3 min to mix well. (2) Place the
   mixed samples into a centrifuge set at 4°C, and centrifuge at
   12,000 r/min for 10 min. (3) Subsequent to centrifugation, the sample
   tube was placed in an icebox, and 200 µL of the supernatant was
   carefully transferred to a new centrifuge tube. This was followed by a
   resting period at −20°C for 30 min. (4) The samples were again
   centrifuged for 3 min at 4°C and 120,000 r/min. (5) After
   centrifugation, 180 µL of the supernatant was collected and
   subsequently placed into the inner liner of the injection vial for
   subsequent analysis. The extraction method for hydrophobic metabolites
   was as follows: (1) the 1 mL lipid internal standards
   (methyl‐tert‐butyl ether: methanol (34885, Merck) = 3:1, v/v) were
   added to the 50 µL sample, and vortexing was carried out for 15 min to
   fully mix the solution. (2) Next, 200 µL of ultrapure water was added
   to the mixed sample and vortexed thoroughly; the mixture was put into a
   centrifuge for 10 min at 4°C and 120,000 r/min after being mixed for
   1 min. (3) About 200 µL of supernatants were recovered into a new
   sterile centrifugation tube, and the sample was then freeze‐dried with
   a concentrator (7310038, LABCONCO, Thermo). (4) A volume of 200 µL of
   mobile phase B, which consists of acetonitrile and isopropanol in a
   ratio of 10:90 (v/v) and is supplemented with 0.1% formic acid
   (F112034, Aladdin) and 10 mmol/L ammonium formate (A100186, Aladdin),
   was introduced into the lyophilization. This mixture was then
   thoroughly vortexed for a duration of 3 min and subsequently subjected
   to centrifugation at 4°C at a speed of 120,000 revolutions per minute.
   (5) After centrifugation, the supernatant was pipetted into autosampler
   vials for LC‐MS/MS analysis.

4.3. Chromatography and Mass Spectrometry Conditions

   In the present investigation, data acquisition employed UPLC‐MS/MS. The
   conditions used for the analysis of hydrophilic metabolites and
   hydrophobic metabolites were different. The liquid phase conditions of
   hydrophilic metabolites were as follows:

   T3 liquid phase was constructed as follows: (1) The chromatographic
   column employed in this study was the Waters ACQUITY UPLC HSS T3 C18,
   characterized by a particle size of 1.8 µm and dimensions of 2.1 mm in
   diameter by 100 mm in length; (2) Mobile phase A consisted of ultrapure
   water supplemented with 0.1% formic acid, while mobile phase B was
   composed of acetonitrile that also contained 0.1% formic acid; (3) The
   gradient elution procedure was conducted as follows: At the outset
   (0 min), a solvent mixture comprising water and acetonitrile in a 95:5
   (v/v) ratio was employed. This composition was progressively altered to
   a 10:90 (v/v) ratio by 11.0 min, which was sustained until 12.0 min.
   Subsequently, at 12.1 min, the elution conditions were reverted to the
   initial 95:5 (v/v) ratio, which was maintained until 14.0 min; (4) The
   parameters for liquid chromatography were set as follows: a flow rate
   of 0.4 mL/min, a column temperature of 40°C, and a sample injection
   volume of 2 µL.

   The conditions for the liquid phase involving amides were described as
   follows: (1) The chromatographic column utilized in this study was the
   Waters ACQUITY UPLC BEH Amide, featuring a particle size of 1.7 µm and
   dimensions of 2.1 mm in diameter by 100 mm in length; (2) Mobile phase
   A consisted of ultrapure water supplemented with 20 mM ammonium formate
   and 0.4% ammonium hydroxide, while mobile phase B was composed
   exclusively of pure acetonitrile; (3) A gradient elution was conducted
   starting with a 10:90 water‐acetonitrile mix at 0 min, shifting to
   40:60 at 9 min, then to 60:40 at 10 min. This 60:40 mix was held until
   11 min before returning to 10:90 at 11.1 min, lasting until the end at
   15 min.

   The conditions for the liquid phase of hydrophobic metabolites were
   established as follows: (1) The chromatographic column utilized in this
   study was a Thermo Accucore C30 with dimensions of 2.1 mm in inner
   diameter and a length of 100 mm, featuring a particle size of 2.6 µm.
   (2) The mobile phase A utilized in this study consisted of a mixture of
   acetonitrile and water in a 60:40 volume ratio, supplemented with 0.1%
   formic acid and 10 mmol/L ammonium formate. Conversely, mobile phase B
   was formulated using acetonitrile and isopropanol in a 10:90 volume
   ratio, also containing 0.1% formic acid and 10 mmol/L ammonium formate.
   (3) A gradient elution was conducted as follows: starting with a ratio
   of A/B (80:20, v/v) at 0 min, it changed to (70:30, v/v) at 2 min,
   (40:60, v/v) at 4 min, (15:85, v/v) at 9 min, and (10:90, v/v) at
   14 min. At 15.5 min, it was adjusted to (5:95, v/v) and held until
   17.3 min. The elution then reverted to (80:20, v/v) at 17.5 min,
   maintaining this until 20 min. (4) The liquid flow rate was
   0.35 mL/min, the column was preheated to 45°C, and the injection volume
   was 2 µL.

   The mass spectrometric conditions of hydrophilic metabolites were as
   follows: (1) the electrospray ionization temperature was set to 500°C,
   and the mass spectrometry voltage was configured to 5500 V in the
   positive ionization mode and −4500 volts in the negative ionization
   mode. The pressure of the ion source Gas I was maintained at 55 psi,
   while Gas II was set to 60 psi. Furthermore, the curtain gas (CUR)
   pressure was adjusted to 25 psi, and the collision‐induced dissociation
   parameter was designated as high. (2) Within the triple quadrupole
   system, each ion underwent scanning and detection through the
   application of optimized clustering potentials and collision energies.

   The mass spectrometric conditions of hydrophobic metabolites were as
   follows: (1) The electrospray ionization process was conducted at a
   temperature of 500°C, utilizing a mass spectrum voltage of 5500 V in
   the positive mode and −4500 V in the negative mode. The pressure of the
   ion source Gas I was maintained at 55 psi, while Gas II was set at
   60 psi. Additionally, the CUR pressure was established at 25 psi, and
   the collision‐activated dissociation was adjusted to a medium setting.
   (2) In the triple quadrupole system, the detection and scanning of each
   ion were conducted utilizing the optimized parameters for clustering
   potential and collision energy.

4.4. Data Processing and Analysis

   The data produced were examined utilizing Analyst version 1.6.3
   software. Metabolites were detected and compared based on the targeted
   standard database MWDB (Metware database), the duration for which the
   identified compounds are retained, and the details regarding the paired
   ions with double charge, and qualitative analysis was conducted on the
   secondary spectrum data. Triple quadrupole mass spectrometry in
   multi‐reaction monitoring mode was utilized to generate peak maps of
   detected substances. The chromatographic peak areas were scored and
   adjusted using MultiQuant software, followed by quantitative analysis
   through peak area integration.

4.5. Bioinformatics and Statistical Analysis

   All statistical analyses of the data were performed utilizing R
   software (version 4.1.1), with the ggplot package employed for the
   purpose of data visualization. The Ropls package facilitated the
   execution of OPLS‐DA, subsequently allowing for the computation of the
   VIP of the metabolites. To identify differential metabolites, a
   combination of VIP, p value, and fold change derived from the
   univariate analysis was utilized. The established cut‐off criteria were
   set as follows: fold change greater than 1.5 or less than 0.66, p value
   below 0.05, and a VIP of at least 1. Furthermore, the MetaboAnalyst
   database ([132]https://www.metaboanalyst.ca/) was employed to conduct
   MSEA. MSEA is a group‐based method that does not need preselected
   metabolites based on the specified threshold, which can avoid missing
   some metabolites with insignificant differential expression but
   important biological meaning. K‐means clustering was employed to
   examine the variations in metabolite levels across different groups.
   Random forest is a decision tree‐based method that was used to evaluate
   the importance of variables based on the randomForest package. The
   Spearman test was used to perform correlation analysis. ANN is a
   high‐dimensional, nonlinear, complex dynamic system. In this study, we
   first defined minimum and maximum values based on the median expression
   value, then constructed an ANN model using the “neuralnet” package, and
   the number of hidden layers was set to 5. Subsequently, the accuracy of
   the ANN model was evaluated using logistic regression analysis. The
   pROC software package was employed to construct ROC curves, followed by
   the calculation of the AUC values. To assess differences within the
   group, the Wilcoxon signed‐rank test was utilized, and statistical
   significance was determined with a p value threshold of below 0.05.

Author Contributions

   J.L.: writing of the article, serum collection, and data analysis.
   Y.Y.: writing – review and editing. Q.L.: serum collection and data
   analysis. S.‐Q.Z.: sample collection. J.‐C.L.: study design, writing –
   review and editing. All authors have read and approved the final
   manuscript.

Ethics Statement

   The study design and protocol were ethically approved by the ethics
   committee of the Yue Bei People's Hospital affiliated with Shantou
   University Medical College (KY‐2021‐050, China). Patients and
   volunteers were informed, and written consents were obtained.

Conflicts of Interest

   The authors declare no conflicts of interest.

Supporting information

   Supporting Information
   [133]MCO2-6-e70303-s001.docx^ (1.7MB, docx)

Acknowledgments