Author + information
- Received March 3, 2016
- Revision received June 7, 2016
- Accepted June 14, 2016
- Published online September 20, 2016.
- Yong Fan, MSa,
- Yong Li, MD, PhDb,
- Yan Chen, MD, PhDc,d,
- Yi-Jing Zhao, BSa,
- Li-Wei Liu, BSa,
- Jin Li, BSa,
- Shi-Lei Wang, BSa,
- Raphael N. Alolga, MSa,
- Yin Yin, MD, PhDe,
- Xiang-Ming Wang, MD, PhDf,
- Dong-Sheng Zhao, MDg,
- Jian-Hua Shen, MD, PhDh,
- Fan-Qi Meng, MD, PhDi,
- Xin Zhou, MD, PhDj,
- Hao Xu, MSb,
- Guo-Ping He, MSb,
- Mao-De Lai, MD, PhDa,
- Ping Li, PhDa,∗∗∗ (, )
- Wei Zhu, MD, PhDj,∗∗ ( and )
- Lian-Wen Qi, PhDa,∗ ()
- aState Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, Jiangsu, China
- bDepartment of Cardiology, the Affiliated Wujin Hospital of Jiangsu University, Changzhou, Jiangsu, China
- cDepartment of Emergency Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- dMedical Department, Women and Children Health Care Hospital of Jiangsu Province, Nanjing, Jiangsu, China
- eDepartment of Gynecology and Obstetrics, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- fDepartment of Geriatric Cardiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- gDepartment of Cardiology, the Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
- hDepartment of Cardiology, Northern Jiangsu People’s Hospital, Yangzhou, Jiangsu, China
- iDepartment of Cardiology, Xiamen Cardiovascular Hospital, Xiamen, Fujian, China
- jDepartment of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- ↵∗Reprint requests and correspondence:
Dr. Lian-Wen Qi, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Department of Pharmacognosy, 24# Tongjia Lane, Zhongyang Road, Nanjing, Jiangsu 210009, China.
- ↵∗∗Dr. Wei Zhu, the First Affiliated Hospital of Nanjing Medical University, Department of Oncology, 300# Guangzhou Road, Nanjing, Jiangsu 210029, China.
- ↵∗∗∗Dr. Ping Li, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Department of Pharmacognosy, 639# Longmian Road, Nanjing, Jiangsu 210009, China.
Background Pathogenesis and diagnostic biomarkers for diseases can be discovered by metabolomic profiling of human fluids. If the various types of coronary artery disease (CAD) can be accurately characterized by metabolomics, effective treatment may be targeted without using unnecessary therapies and resources.
Objectives The authors studied disturbed metabolic pathways to assess the diagnostic value of metabolomics-based biomarkers in different types of CAD.
Methods A cohort of 2,324 patients from 4 independent centers was studied. Patients underwent coronary angiography for suspected CAD. Groups were divided as follows: normal coronary artery (NCA), nonobstructive coronary atherosclerosis (NOCA), stable angina (SA), unstable angina (UA), and acute myocardial infarction (AMI). Plasma metabolomic profiles were determined by liquid chromatography–quadrupole time-of-flight mass spectrometry and were analyzed by multivariate statistics.
Results We made 12 cross-comparisons to and within CAD to characterize metabolic disturbances. We focused on comparisons of NOCA versus NCA, SA versus NOCA, UA versus SA, and AMI versus UA. Other comparisons were made, including SA versus NCA, UA versus NCA, AMI versus NCA, UA versus NOCA, AMI versus NOCA, AMI versus SA, significant CAD (SA/UA/AMI) versus nonsignificant CAD (NCA/NOCA), and acute coronary syndrome (UA/AMI) versus SA. A total of 89 differential metabolites were identified. The altered metabolic pathways included reduced phospholipid catabolism, increased amino acid metabolism, increased short-chain acylcarnitines, decrease in tricarboxylic acid cycle, and less biosynthesis of primary bile acid. For differential diagnosis, 12 panels of specific metabolomics-based biomarkers provided areas under the curve of 0.938 to 0.996 in the discovery phase (n = 1,086), predictive values of 89.2% to 96.0% in the test phase (n = 933), and 85.3% to 96.4% in the 3-center external sets (n = 305).
Conclusions Plasma metabolomics are powerful for characterizing metabolic disturbances. Differences in small-molecule metabolites may reflect underlying CAD and serve as biomarkers for CAD progression.
Coronary artery disease (CAD) remains a leading cause of mortality worldwide. According to the Global Burden of Disease Study 2013, CAD was responsible for an estimated 8.14 million deaths (16.8%) globally that year (1). On the basis of clinical symptoms, extent of arterial blockage, and myocardial injury, CAD is divided into different categories: nonobstructive coronary atherosclerosis (NOCA), stable angina pectoris (SA), unstable angina pectoris (UA), and acute myocardial infarction (AMI) (2). UA and AMI are also referred to as acute coronary syndrome (ACS).
If the molecular mechanisms of CAD could be deciphered, its incidence and associated mortality might be reduced. Multiple, complex molecular events characterize the progression of CAD. Atherosclerosis, a common cause of angina and AMI, is a slow and complicated process (3). What exactly causes plaque, how plaque develops over time, and why plaque dislodges to form a clot are largely unknown. Experimental models of atherogenesis provide information about the molecular mechanisms of plaque growth. Nevertheless, the transition from coronary stability to instability is less well understood because animal models of this progression are unavailable (4).
With early screening and differential diagnosis of CAD, optimal patient-specific therapies can be initiated. The current clinical diagnosis differentiates between the types of CAD on the basis of symptoms, electrocardiogram (ECG), cardiac markers, stress testing, and coronary angiography (5–7). Among these methods, invasive coronary angiography is the diagnostic “gold standard” (8), but its specialized technology and high cost limit it to a select population (5). On the one hand, a sizable portion of individuals who underwent invasive angiography had been shown to have normal coronary arteries (9). On the other hand, episodes of myocardial ischemia or infarction are possible after atypical symptoms in some patients with CAD, especially in patients who are elderly or have diabetes (10).
Abnormal metabolism also characterizes CAD. Metabolic alterations in the heart result in changes in the metabolome of biofluids (11). Metabolites could clarify pathogenesis for potential therapeutic targets. A combination of multiple small-molecule metabolites may offer excellent diagnostic values (12). Metabolomics, a rapidly expanding field in systems biology, measures metabolic alterations in response to disease progression (12). Plasma, a frequently considered pool of metabolites, has been a source for metabolic profiling (13). Liquid chromatography mass spectrometry provides the most compatible technique for sensitive detection of small-molecule metabolites with robust reliability and reproducibility (14,15). This work describes a comprehensive metabolomic evaluation for identifying types of CAD.
Patients and study design
Patients enrolled from center 1 (the Affiliated Wujin Hospital of Jiangsu University, Changzhou, China) between August 2009 and December 2015 formed the discovery and test phases. Subjects recruited at 3 other centers constituted the external validation phase between January 2014 and December 2015 (center 2, the First People’s Hospital, Nantong, China; center 3, Northern Jiangsu People’s Hospital, Yangzhou, China; and center 4, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China).
Inclusion criteria were symptoms of chest pain, cardiovascular risk factors, ischemic changes in ECG, or elevated myocardial enzymes. Coronary angiography was required to confirm the diagnosis. We excluded patients with aortic dissection, pulmonary embolism, malignant tumor, autoimmune disorders, severe infectious diseases, trauma, a recent surgical procedure, severe heart failure with left ventricular ejection fraction <20%, liver dysfunction (alanine aminotransferase level >135 U/l), severe renal dysfunction (creatinine >3.0 mg/dl), or blood-borne infectious diseases, including human immunodeficiency virus/acquired immunodeficiency syndrome, hepatitis B, and hepatitis C. We also excluded patients with myocarditis, pericarditis, and Takotsubo cardiomyopathy. Informed consent was obtained from all patients. This study was performed under the guidance of the Helsinki Declaration and was approved by all centers.
Plasma samples were collected before the coronary angiography surgery and were immediately frozen at −80°C for metabolomic analyses. Acetonitrile was chosen as the optimal extraction solvent over methanol, ethanol, methanol/ethanol (1:1), and methanol/acetonitrile/acetone (1:1:1). To ensure data quality for metabolic profiling, pooled quality control samples were prepared by mixing equal amounts of plasma (10 μl) from 82 patients with normal coronary arteries and 125 patients with CAD. Detailed sample preparation methods are in the Online Appendix.
Definition of coronary artery types
The diagnosis was made on the basis of symptoms, laboratory tests, ECG, and coronary angiographic results. Patients with no stenosis in coronary arteries comprised the normal coronary artery (NCA) group, which also included patients with myocardial bridging, intercostal neuralgia, reflux esophagitis, cervical spondylopathy, and unexplained chest pain. In the AMI group, patients had ischemic chest pain, increased values of cardiac enzymes, and dynamic ST-T change on ECG. In the UA group, patients had a history of angina (within 1 month), irregular angina at rest or with minimal exertion, and no elevation in troponin. The SA group included patients with angina of stable frequency, lasting up to 10 min, or provocative-palliative factors. Patients with <50% stenosis in all coronary vessels were in the NOCA group. The angiographic data were confirmed independently by 2 observers in each study.
Liquid chromatographic separation for processed plasma was achieved on a 100 × 2.1-mm Zorbax Eclipse Plus 1.8-μm C18 column using a 1290 Infinity System, whereas mass spectrometry was performed on a 6530 Quadrupole-Time of Flight system (all devices from Agilent Technologies, Santa Clara, California). Samples were assigned by the study administrator with a random-number generator in Excel (Microsoft, Redmond, Washington) to receive 3 sequences for samples in the discovery phase from center 1, test phase from center 1, and external validation phases from centers 2 to 4. Samples from the 4 independent centers were then alternated and analyzed according to the generated random order. During analyses of the sample sequence, 1 quality control sample was run after every 20 injections. The acquired mass spectrometry data (.d) were exported to data format (.mzdata) files by MassHunter Workstation Software (version B.06.00, Agilent Technologies). Data pre-treatment procedures, such as nonlinear retention time alignment, peak discrimination, filtering, alignment, matching, and identification, were performed in XCMS package (Scripps Center for Metabolomics and Mass Spectrometry, La Jolla, California). Detailed parameters for the data analyses are provided in the Online Appendix. Open database sources, including the KEGG, MetaboAnalyst, Human Metabolome Database, and METLIN, were used to identify metabolic pathways.
Results are expressed as the mean ± SD for continuous variables and as the number (percent) for categorical variables. To maximize identification of differences in metabolic profiles between groups, the orthogonal projection to latent structure-discriminant analysis (OPLS-DA) model was applied using SIMCA version 14.0.1 (Umetrics AB, Umea, Sweden). In addition to the multivariate statistical method, the Student t test was also applied to measure the significance of each metabolite. The resultant p values for each metabolite in all cross-comparisons were corrected by Bonferroni correction. The p values across all metabolites within each comparison were adjusted to account for multiple testing by a false discovery rate method. Heat maps and hierarchical cluster analyses were conducted using MeV version 4.6.0. The Cytoscape software package version 3.2.0 (National Institute of General Medical Sciences, Bethesda, Maryland) was used to plot the correlation networks. Logistic regression analysis and receiver operating characteristic (ROC) analysis were used for diagnosis of different CAD stages. Statistical analyses were performed using SPSS software version 19.0 (IBM Corp., Armonk, New York). An adjusted p value of <0.05 was considered statistically significant.
A total of 2,324 patients were enrolled from 4 independent centers in China (Figure 1). All participants underwent coronary angiography to identify the location and extent of the blockage. As shown in Online Figure 1, there was a progressive increase in stenosis severity across the groups from NOCA to SA, UA, and then AMI.
The discovery phase included 1,086 randomly selected participants from center 1, including from the following groups: 116 NCA, 276 NOCA, 63 SA, 307 UA, and 324 AMI patients. Baseline characteristics and laboratory data are shown in Table 1. Compared with NCA subjects, CAD patients had higher levels of hemoglobin, glucose, and estimated glomerular filtration rate, but lower levels of total cholesterol as well as low- and high-density lipoprotein cholesterol. CAD patients were older than subjects without coronary diseases, although there were no statistically significant differences in age among the patient groups. The percentage of females was lower among CAD patients than in NCA subjects.
The test phase from center 1 included 933 participants. Their baseline characteristics are shown in Online Table 1, and numbers participating in the external validation phase are shown in Figure 1. Demographic information of external validation batches is shown in Online Tables 2 to 4.
Cross comparisons to and within CAD groups
Typical total ion chromatograms of the 5 representative samples obtained are presented in Online Figure 2. After peak alignment and removal of missing values, 2,032 positive-mode features were detected. A total of 1,130 ions had significantly changed (p < 0.05). The OPLS-DA model was used to characterize the metabolic disturbances. The ions with variable importance in the projection (VIP) values >1.0 were considered the potential differential metabolites. We compared the various stages of CAD to normal coronary arteries and with each other, identifying and characterizing specific metabolites and metabolic pathways. We focused on NOCA versus NCA for plaque formation, SA versus NOCA for plaque growth, UA versus SA for transition from coronary stability to instability, and AMI versus UA.
Clear differences were obtained for the following: NOCA versus normal arteries, cumulative R2Y at 0.655 and Q2 at 0.503 (Figure 2A); SA versus NOCA, R2Y at 0.626 and Q2 at 0.518 (Figure 2B); UA versus SA, R2Y at 0.645 and Q2 at 0.548 (Figure 2C); and AMI versus UA, R2Y at 0.641 and Q2 at 0.595 (Figure 2D). The metabolites with VIP values >1.0 and adjusted p values <0.05 for each comparison appear in Online Table 5. Figures 2E to 2H represent the perturbed pathways. For NOCA versus NCA, metabolism changed for glycerophospholipid, purine, and sphingolipid; for SA versus NOCA, glycerophospholipid, valine-leucine-isoleucine, primary bile acid biosynthesis, and arginine-proline; for UA versus SA, glycerophospholipid, alanine-aspartate-glutamate, and arginine-proline; and for AMI versus UA, glycerophospholipid, sphingolipid, and arginine-proline. Disturbance of glycerophospholipid metabolism was the most significant in all paired comparisons. The OPLS-DA plots of 8 other cross-comparisons, including SA versus NCA, UA versus NCA, AMI versus NCA, UA versus NOCA, AMI versus NOCA, AMI versus SA, significant CAD (SA/UA/AMI) versus nonsignificant CAD (NCA/NOCA), and ACS (UA/AMI) versus SA, are shown in Online Figure 3. The differential metabolites and altered metabolic pathways for these comparisons appear in Online Table 5 and Online Figure 4, respectively.
Correlation network of differential metabolites in CAD plasma
By 12 comprehensive cross-comparisons of different CAD groups, 89 differential metabolites were identified, from which 44 were confirmed using reference compounds. The concentrations of these 89 metabolites in normal arteries and the CAD groups are summarized in Online Tables 6 to 8. Figure 3A is a heat map showing the average normalized quantities of the 89 differential metabolites in normal arteries and the CAD groups. Among them, 27 metabolites were elevated; 62 metabolites decreased in order according to the following groups: NCA, NOCA, SA, UA, and AMI.
To investigate the latent relationships of the 89 differential metabolites, the Pearson correlation coefficients between the metabolites were calculated on the basis of the average normalized quantities of metabolites (Online Figure 5). Highly correlated metabolites with |r| > 0.9 are connected with lines in the network for a view of the disturbed metabolism of CAD (Figure 3B). Most metabolites in tricarboxylic acid cycle and phospholipids were down-regulated in CAD groups and had highly correlated coefficients. Levels of some correlated amino acids were up-regulated. Short-chain acylcarnitines, in the center of the network, bridged the altered metabolites. Metabolites of bile acids decreased. The disturbed metabolic pathways are detailed in Online Figure 6 on the basis of the KEGG Pathway Database.
Differential diagnosis of metabolic biomarkers
Accurate diagnosis of the stage of CAD is fundamental for personalized treatment of coronary disease. For differential diagnosis, the criteria of metabolomics-based biomarkers are VIP >1.5 and adjusted p value <0.05. As summarized in Table 2, there were 10 specific metabolic biomarkers for distinguishing NOCA from NCA, 8 for SA versus NOCA, 10 for UA versus SA, and 9 for AMI versus UA; additional metabolomics-based biomarker comparisons are provided in Online Table 5.
The ROC presentations, on the basis of the logistic regression of each biomarker panel from the discovery phase, appear in Figure 4; the areas under the curve (AUC), sensitivity, and specificity are 0.952, 94.2%, and 80.7% for NOCA versus NCA (n = 392) (Figure 4A); 0.993, 96.4%, and 95.6% for SA versus NOCA (n = 339) (Figure 4B); 0.990, 97.4%, and 91.1% for UA versus SA (n = 370) (Figure 4C); and 0.992, 94.5%, and 95.3% for AMI versus UA (n = 631) (Figure 4D), respectively. For additional cross-comparisons, AUCs ranged from 0.938 to 0.996, sensitivities from 83.3% to 99.1%, and specificities from 90.9% to 97.2% (Online Figure 7). ROC curves with AUC, sensitivity, and specificity values for the 12 cross-comparisons in the test phases and 3-center external validation sets are shown in Online Figure 8. Logistic regression analysis of diagnostic biomarkers in all cross-comparisons appears in Online Table 9.
On the basis of the highest prediction sensitivity and specificity of the ROC in the discovery phase, the optimal cut-off values were 0.635 for NOCA versus NCA, 0.205 for SA versus NOCA, 0.692 for UA versus SA, and 0.475 for AMI versus UA. The cut-off values were then used to predict the different stages of CAD in the test phase and external sets. Predictive value was 95.0% for NOCA versus NCA in the test phase and 91.5% in the 3-centered external validation sets (Figure 4E); 94.5% for SA versus NOCA in the test phase and 89.7% in the validation sets (Figure 4F); 91.8% for UA versus SA in the test phase and 96.4% in the validation sets (Figure 4G); and 96.0% for AMI versus UA in the test phase and 85.3% in the validation sets (Figure 4H). Predictive values for additional comparisons ranged from 89.2% to 95.0% in the test phase and from 86.8% to 94.5% in the 3-centered external validation sets (Online Figure 9). Odds ratios of the 12 biomarker panels after adjustment for possible confounding comorbidities, and concomitant treatments in all cross-comparisons are provided in Online Table 10. The 12 CAD-associated plasma metabolite signatures remained significant (adjusted p value <0.05) after adjustment for comorbidities and concomitant treatments.
This work described a comprehensive metabolomic evaluation for 2,324 patients who underwent coronary angiography in 4 independent centers. Metabolic phenotypes revealed significant pattern differences between patients with CAD and no coronary disease and within CAD types. With 89 significantly regulated metabolites in plasma samples, the changes suggested that CAD may involve a universal metabolic disturbance. Phospholipid catabolism and the tricarboxylic acid cycle decreased, amino acid metabolism and short-chain acylcarnitines increased, and primary bile acid biosynthesis declined during CAD progression. Combinations of metabolic biomarkers offered excellent predictive values for distinguishing each CAD type from normal coronary arteries and from one another (Central Illustration).
The group without coronary disease had 335 (14.4%) participants who experienced angina-like chest pain and discomfort but had a normal artery confirmed by angiography. Comparison with this group made possible distinctions in patients with confusing symptoms. The extent of coronary artery blockage revealed positive correlations with the severity of CAD. All the NOCA patients had <50% stenosis. As calculated, 63.2% of SA, 61.8% of UA, and 71.4% of AMI patients showed blockage ≥70%. In contrast, only a small portion of patients with UA or AMI had a blockage <50%.
The exact cause of NOCA, which produces angina and AMI, remains largely unknown. Compared with patients with normal coronary arteries, patients with NOCA had down-regulated lysophosphatidylcholines, lysophosphatidylethanolamine 18:2, and phosphatidylethanolamine and up-regulated phytosphingosine. As shown in Online Figure 10, a large portion of lysophosphatidylcholines and lysophosphatidylethanolamines in plasma was generated from phosphatidylcholines and phosphatidylethanolamines by the activity of lecithin cholesterol acyltransferase. Low lecithin cholesterol acyltransferase activity has been linked to CAD (16,17). Elevated levels of sphingolipids are characteristic of obesity and cardiovascular risks (18).
Plaques from atherosclerosis grow to induce significant stenosis in a coronary artery. Compared with the NOCA, the SA group had down-regulation of phosphocholine, phosphatidylethanolamine, phosphatidylcholine, ethylchenodeoxycholic acid, lysophosphatidylcholine 16:0, and lysophosphatidylcholine 18:2, and up-regulation of phytosphingosine and phosphatidylinositol 20:4/0:0. Usually, phosphatidylcholine is metabolized by intestinal microbiota to produce proatherogenic species, choline, and trimethylamine oxide. The decrease of phosphatidylcholine has implications for CAD (19). Ethylchenodeoxycholic acid activates the farnesoid X receptor, an endogenous sensor for bile acids. The inverse correlation between it and CAD has been reported (20). Phosphatidylinositol, an activator of intracellular Ca2+, was up-regulated in SA. Increased phosphatidylinositol 20:4/0:0 affirmed the serious vascular calcification in patients with SA (21).
When coronary arteries are severely narrowed by atherosclerosis or when plaques rupture and produce clots, angina pectoris becomes unstable. The transition from coronary stability to instability is less well understood. Compared with stable angina, unstable angina up-regulates creatine, 2-hydroxylauric acid, tryptophan, isobutyrylcarnitine, propionylcarnitine, and acetylcarnitine, and down-regulates aspartic acid, phosphocholine, lysophosphatidylcholine 16:0, and lysophosphatidylcholine 18:1. The increased creatine corresponds to a decrease in the activity of the creatine kinase system. The creatine kinase system protects the cardiovascular system from ischemia and increases contractility (22). 2-hydroxylauric acid is a medium-chain fatty acid associated with fatty acid metabolic disorders during plaque rupture (23). Tryptophan is closely associated with immune system activation and inflammation (24). With the detection of tryptophan, we assumed that the levels of inflammation and immune activity increased in UA compared with SA. Elevated levels of short-chain acylcarnitines suggest activated fatty acid metabolism in UA (25). Reduced aspartic acid corresponds to the high risk of myocardial damage in UA (26). Phosphocholine was reduced more in UA than SA.
Compared with UA, AMI up-regulated N-phenylacetyl-L-glutamine, sphinganine, eicosatertraenoic acid, eicosatrienoic acid, and tryptophan-arginine-leucine, and reduced glycocholic acid, lysophosphatidylcholine 14:0, lysophosphatidylcholine 18:2, and lysophosphatidylcholine 20:3. The elevation of N-phenylacetyl-L-glutamine suggests a perturbed phenylalanine metabolism during the transition from UA to AMI (27). Up-regulation of sphinganine, an intermediate of sphingoid base biosynthesis (28), suggests that sphingolipid metabolism is hampered in AMI. Studies have linked elevated sphingolipids to cardiovascular diseases and obesity, confirming our findings. The presence of eicosatertraenoic acid and eicosatrienoic acid in greater proportions reflects the inflammation experienced in AMI (29). Glycocholic acid, crucial in bile acid synthesis and cholesterol metabolism (30), was down-regulated in patients with AMI because the metabolism of cholesterol and phospholipids was inhibited (31). Tryptophan-arginine-leucine, an ingredient to generate amino acids, was at high levels in these patients. Our results proved that activated amino acid biosynthesis is an indicator for AMI (32).
The potential applications in clinical diagnosis included: 1) differential diagnosis of significant versus nonsignificant CAD; 2) if CAD is not significant, a differential diagnosis of NOCA versus NCA is required; 3) if CAD is significant, a differential diagnosis of ACS versus SA is required; and 4) if not SA, a differential diagnosis of AMI versus UA is required. The sensitivities, specificities, and predictive values of the metabolic biomarkers we discovered were much higher than reported with noninvasive methods such as coronary computed tomographic angiography (33). These biomarkers separate individuals who may benefit from additional testing with invasive angiography from individuals who will not.
First, the use of this technique could exclude other classes of lipids and amino acids. A dual analytical platform, such as gas chromatography mass spectrometry together with liquid chromatography–mass spectrometry, is recommended in future studies. Second, because of lacking a centralized core laboratory, it is possible there were site-to-site and observer-to-observer variations in the adjudication of coronary stenosis. Third, our study population consisted of middle-aged to elderly Chinese patients. In future studies, the scope could be broadened to include other ethnicities within Asia and other races such as Caucasians and Africans. Younger age groups suspected of or confirmed with CAD could be considered.
We reported a comprehensive metabolomic evaluation for identifying clinically relevant perturbations in circulating metabolites in CAD. This evaluation improved the understanding of CAD pathogenesis and facilitates target screening for therapeutic intervention. Novel biomarkers predict and differentiate between CAD types; such differentiation may reduce unnecessary invasive coronary angiography, enhance predictive value, and complement current diagnostic methods.
COMPETENCY IN MEDICAL KNOWLEDGE: Chromatographic analysis of human plasma can identify metabolic abnormalities of phospholipid catabolism; amino acid metabolism; and synthesis of short-chain acylcarnitines, tricarboxylic acid cycle byproducts, and primary bile acids that are associated with various clinical presentations of CAD.
TRANSLATIONAL OUTLOOK: Additional studies are needed to correlate the effects of therapeutic interventions for patients with different forms of CAD with plasma levels of metabolic biomarkers.
The authors thank Sally Kozlik from the University of Chicago for editing our manuscript.
For a supplemental Methods section as well as figures and tables, please see the online version of this article.
This study was supported in part by the National Natural Science Foundation of China (81421005, 81571873), National Excellent Doctoral Dissertation of PR China (201278), and Jiangsu Province Science Fund for Distinguished Young Scholars (BK20130025). The authors have reported that they have no relationships relevant to the contents of this paper to disclose. Drs. Yong Fan, Yong Li, and Yan Chen contributed equally to this work.
- Abbreviations and Acronyms
- acute myocardial infarction
- area under the curve
- coronary artery disease
- normal coronary artery
- nonobstructive coronary atherosclerosis
- orthogonal projection to latent structure-discriminant analysis
- receiver-operating characteristic
- stable angina
- unstable angina
- variable importance in the projection
- Received March 3, 2016.
- Revision received June 7, 2016.
- Accepted June 14, 2016.
- American College of Cardiology Foundation
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