Abstract
OBJECTIVES
To analyze the differential expression of lipid spectrum between ST-segment elevated myocardial infarction (STEMI) and patients with emergency chest pain and excluded coronary artery disease (CAD), and establish the predictive model which could predict STEMI in the early stage.
METHODS
We conducted a single-center, nested case-control study using the emergency chest pain cohort of Peking University Third Hospital. Untargeted lipidomics were conducted while LASSO regression as well as XGBoost combined with greedy algorithm were used to select lipid molecules.
RESULTS
Fifty-two STEMI patients along with 52 controls were enrolled. A total of 1925 lipid molecules were detected. There were 93 lipid molecules in the positive ion mode which were differentially expressed between the STEMI and the control group, while in the negative ion mode, there were 73 differentially expressed lipid molecules. In the positive ion mode, the differentially expressed lipid subclasses were mainly diacylglycerol (DG), lysophophatidylcholine (LPC), acylcarnitine (CAR), lysophosphatidyl ethanolamine (LPE), and phosphatidylcholine (PC), while in the negative ion mode, significantly expressed lipid subclasses were mainly free fatty acid (FA), LPE, PC, phosphatidylethanolamine (PE), and phosphatidylinositol (PI). LASSO regression selected 22 lipids while XGBoost combined with greedy algorithm selected 10 lipids. PC (15: 0/18: 2), PI (19: 4), and LPI (20: 3) were the overlapping lipid molecules selected by the two feature screening methods. Logistic model established using the three lipids had excellent performance in discrimination and calibration both in the derivation set (AUC: 0.972) and an internal validation set (AUC: 0.967). In 19 STEMI patients with normal cardiac troponin, 18 patients were correctly diagnosed using lipid model.
CONCLUSIONS
The differentially expressed lipids were mainly DG, CAR, LPC, LPE, PC, PI, PE, and FA. Using lipid molecules selected by XGBoost combined with greedy algorithm and LASSO regression to establish model could accurately predict STEMI even in the more earlier stage.
The 2019 global burden of disease report showed that acute coronary syndrome (ACS) was one of the main causes of human death.[1] ST-segment elevated myocardial infarction (STEMI) is the most serious subtype of ACS with the worst prognosis. Early and accurate diagnosis is the key to reduce the mortality of STEMI patients. Chest pain is one of the most common complaints of patients with STEMI, which accounts for about 5% to 10% of all emergency patients.[2] The etiology of patients with chest pain as the main complaint is diverse, while more than 50% of patients are finally confirmed as chest pain of non-cardiogenic reasons.[3] Accurate diagnosis of STEMI is of great significance to improve the prognosis of STEMI patients and reduce medical costs.
As a practical and convenient tool, biomarkers are often used in disease diagnosis, monitoring treatment response, evaluating prognosis and conducting risk stratification.[4] At present, exploring valuable new biomarkers has been a hotspot for researchers. Abnormal lipid metabolism plays an important role in the occurrence and development of STEMI. Till now, the most widely studied lipids were mainly lipid macromolecules, such as low density lipoprotein cholesterol (LDL) and lipoprotein (a), which were mainly used in clinic as risk factors for atherosclerosis.[5] The concentration of small molecule metabolites is highly sensitive to biological activity and pathological conditions. The small molecule metabolites are more reliable to reflect the state of biological system in the early stage which can be potential ideal biomarkers.
Metabolomics is a subject with small molecule metabolites as the research object. By measuring the overall expression and changes of small molecule metabolites, it is used to further screen for biomarkers and study biological mechanisms. As a branch of metabolomics, lipidomics is a discipline based on mass spectrometry (MS) to comprehensively evaluate the distribution of lipids. Lipidomics takes small molecule lipid metabolites as the research object, which including untargeted lipidomics and targeted lipidomics.[6,7] Liquid chromatography/mass spectrometry (LC/MS) is a separation and analysis technology with relatively high resolution. Its high sensitivity and wide dynamic range make it the most commonly used technology in lipidomics researches. At present, few studies focused on lipidomics in patients with STEMI.
In this study, we aimed to analyze the differential expression of lipid spectrum between STEMI and patients excluded CAD, using untargeted lipidomics based on LC/MS, screen small molecular lipid metabolites using eXtreme Gradient Boosting (XGBoost) combined with greedy algorithm and LASSO regression, and establish predictive model which could predict STEMI in the early stage.
METHODS
Study Design
This is a single-center, nested case-control study conducted in accordance with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement.[8] This study was conducted in accordance with the Declaration of Helsinki and with approval from the ethics committee of Peking University Third Hospital.
Study Population and Data Collection
Patients came to the emergency department of Peking University Third Hospital with chest pain as chief complaint forming the emergency chest pain cohort from March 2010 to January 2014. Patients diagnosed as STEMI (STEMI group) as well as patients excluded CAD (control group) aged over 18 years were enrolled in this study. Patients were excluded in this study if they have one of the following situations: (1) severe liver and kidney insufficiency; (2) combined with thyroid diseases, including hyperthyroidism, hypothyroidism, and thyroiditis; (3) malignant tumor; (4) infection or inflammatory diseases; and (5) other heart diseases, such as congenital heart disease, valvular heart disease, cardiomyopathy and severe arrhythmia. Patients were diagnosed as STEMI according to the Fourth universal definition of myocardial infarction (MI).[9] Patients were included in the control group if they did not meet the diagnostic criteria of STEMI,[9] non-STEMI,[10] unstable angina,[10] and chronic coronary disease,[11] and had other definite causes of chest pain. Propensity score matching (PSM) was used to match STEMI patients with controls with the ratio of 1: 1 based on age and sex. The demographic characteristics including sex, age, past history including hyperlipidemia (HL), diabetes mellitus (DM), hypertension (HT), MI, cerebrovascular disease (CVD), and smoking history were collected.
We recorded the time the patient arrived at the emergency department to calculate the duration of chest pain at admission. The first venous blood sample were collected and were allowed to stand at room temperature (37 °C) for 30 min. After centrifugation at 3500−4000 r/min at room temperature for 5−10 min, the upper supernatant of the blood sample was loaded into a new Eppendorf tube which was stored in refrigerator at −80 °C.
Untargeted LC/MS Analysis
The sample of quality control (QC) was obtained by forming a mixed pool of different serum samples. For untargeted reverse-phase LC-MS analysis, sample preparation was conducted as previously described.[12] Briefly, 100 μL liquid–liquid extraction solution was added to 25 μL serum, subjected to vibration and centrifugation and the lower organic phase was collected and evaporated under vacuum. The Ultimate 3000 UHPLC system (Thermo) and Acquity CSH C18 column (100 × 2.1 mm2, i.d., 2.5 μm, Waters) were used for LC separation. The column temperature was set to 50 °C. Q-Exactive (hybrid quadrupoleOrbitrap mass spectrometer) coupled with heated electrospray ionisation (HESI) source (Thermo Fisher Scientific) was used for mass analysis in data-dependent acquisition (DDA) mode. Other parameters used were as previously described.[12] QC samples were analysed repeatedly in the batch of sample acquisition to evaluate the stability of the LC-MS instrument. All samples are separately acquired in the positive and negative ion mode.
Bioinformatics Analysis
The orthogonal partial least-squares discrimination analysis (OPLS-DA) and the cluster analysis were used to preliminarily evaluate the inter group differences of lipid molecules. Fold change (FC) and false discovery rate (FDR) were used to screen differential metabolites. FC ≥ 1.5 (up-regulated) or FC ≤ 0.67 (down-regulated) and FDR < 0.05 were the main standard for selecting differential lipid molecules.
Statistical Analysis
Statistical analysis and generation of graph were performed using R 4.0.3. Continuous variables conformed to the normal distribution were expressed as the mean ± standard deviation, and t-test was used for comparison between two groups. Medians (interquartile range) and Wilcoxon tests were used for continuous variables which were not conformed to normal distribution. Categorical variables were presented as percentages with Chi-squared test or Fisher’s exact test for comparison between two groups. PSM was used to match STEMI patients with control group patients with the ratio of 1: 1 based on age and sex, using the package ‘MatchIt’ of R. LASSO regression using package ‘glmnet’ and XGBoost using the package ‘xgboost’ were conducted to perform the feature selection process. We used greedy algorithm along with XGBoost to select significantly differential expressed lipid molecules. The specific methods are as follows. Firstly, we used XGBoost to calculate and rank the weights of all lipid metabolites with significant differences. Lipid metabolites were put into the XGBoost model from high to low according to the weight ranking. Then we used the 4-fold cross validation to calculate the average value of the area under the curve (AUC) after each variable was included, and selected the minimum number of molecules reaching the highest AUC level. We used the consistent variables among the variables screened by LASSO regression and XGBoost model as the lipid metabolites finally included in the logistic regression model. We used a 1000 times bootstrap process to conduct the internal validation of the model. The receiver operating characteristic curve (ROC) was used to evaluate the discrimination of the model while a fifth quantile calibration curve was used to assess the calibration of the model. The positive predicted value (PPV), negative predicted value (NPV), sensitivity, specificity, and accuracy were calculated to evaluate the diagnostic efficacy of the model.
RESULTS
Among the 391 patients in the emergency chest pain cohort, 52 STEMI patients along with 52 control group patients were enrolled in this study (Figure 1).
Figure 1.
The workflow of this study.
CAD: coronary artery disease; LC/MS: liquid chromatography/mass spectrometry; PSM: propensity score matching; STEMI: ST-segment elevated myocardial infarction.
Baseline Characteristics
There were no significant differences between STEMI and control in sex (male, 75% vs. 80.8%, P = 0.637), age (60.6 ± 14.7 vs. 55.9 ± 13.5 years, P = 0.091), DM (13.5% vs. 7.7%, P = 0.524), MI (9.6% vs. 0%, P = 0.057), and CVD (7.7% vs. 3.9%, P = 0.678). There were significantly more patients with HL (34.6% vs. 5.8%, P = 0.001), HT (59.6% vs. 13.5%, P < 0.001), and smoking history (46.2% vs. 3.9%, P < 0.001). Baseline characteristics were showed in Table 1.
Table 1. Baseline characteristics between STEMI and control group.
| Variables | STEMI (n = 52) | Control (n = 52) | P-value |
| Data are presented as mean ± SD or n (%). CVD: cerebrovascular disease; DM: diabetes mellitus; HL: hyperlipidemia; HT: hypertension; MI: myocardial infarction; STEMI: ST-segment elevated myocardial infarction. | |||
| Demographic characteristics | |||
| Sex | 39 (75.0%) | 42 (80.8%) | 0.637 |
| Age, yrs | 60.6 ± 14.7 | 55.9 ± 13.5 | 0.091 |
| Past history | |||
| HL | 18 (34.6%) | 3 (5.8%) | 0.001 |
| DM | 7 (13.5%) | 4 (7.7%) | 0.524 |
| HT | 31 (59.6%) | 7 (13.5%) | < 0.001 |
| MI | 5 (9.6%) | 0 | 0.057 |
| CVD | 4 (7.7%) | 2 (3.9%) | 0.678 |
| Smoke | 24 (46.2%) | 2 (3.9%) | < 0.001 |
Untargeted Lipidomics Analysis
A total of 1925 lipid molecules were detected, with 1020 in positive ion mode and 905 in negative ion mode. The correlation analysis of QC samples in the positive and negative ion mode was conducted respectively. The results showed that the Pearson correlation coefficient of QC samples in positive and negative ion mode were all greater than 0.97, indicating that the experimental conditions and system instruments were stable in the process of LC/MS, and the original experimental data have high reliability (Figure 2).
Figure 2.
Quality control of the LC/MS test.
(A): Positive ion mode; and (B): negative ion mode. QC: quality control; LC/MS: liquid chromatography/mass spectrometry.
The OPLS-DA analysis showed that the overall difference of lipid molecules between the STEMI and the control group was significant in both positive ion mode and negative ion mode (Figure 3A & 3B). After screening process, a total of 93 lipid molecules in positive ion mode were found to have significant differences between the STEMI and the control group, of which 65 were up-regulated and 28 were down regulated (Figure 3C). In the negative ion mode, there were 73 significantly different lipid molecules, with 58 up-regulated lipids and 15 down-regulated lipids (Figure 3D).
Figure 3.
The OPLS-DA and differential analysis.
(A): Positive ion mode; (B): negative ion mode; (C): positive ion mode; and (D): negative ion mode. FC: fold change; OPLS-DA: orthogonal partial least-squares discrimination analysis; STEMI: ST-segment elevated myocardial infarction.
Cluster analysis also showed that there were significant differences in lipid molecules between the STEMI and the control group (Figure 4A). There are many kinds of lipid metabolites, and their classification is the basis of studying lipid metabolism. At present, the general lipid classification method divides lipids into eight lipid categories, and each category is divided into multiple lipid main classes.[13] The main categories continue to be divided into more lipid subclasses. Finally, each subclass contains different lipid molecules. Differentially expressed lipid molecules between the STEMI and control were concentrated in five lipid categories including fatty acyls (FAs), glycerolipids (GL), glycerophospholipids (GP), sphingolipids (SP), and sterol lipids (ST) (Figure 4A). In the positive ion mode, the significantly expressed lipid profiles included GL (37, 39.8%), GP (33, 35.5%), FAs (11, 11.8%), SP (8, 8.6%), and ST (4, 4.3%) (Figure 4B), while in the negative ion model, different lipid classes were GP (54, 74%), FA (12, 16.4%), and SP (7, 9.6%) (Figure 4C). As respect to the lipid subclass, in the positive ion mode, the lipid molecules with significant differences were mainly diacylglycerol (DG) (32, 34.4%), acylcarnitine (CAR) (11, 11.8%), lysophophatidylcholine (LPC) (12, 12.9%), lysophosphatidyl ethanolamine (LPE) (6, 6.5%), and phosphatidylcholine (PC) (9, 9.7%), which were mainly up-regulated (Figure 5A, Figure 5C). In the negative ion mode, significantly different lipid subclasses were mainly free fatty acid (FA) (10, 13.7%), LPE (13, 17.8%), PC (7, 9.6%), phosphatidylethanolamine (PE) (10, 13.7%), and phosphatidylinositol (PI) (6, 8.2%), which were mainly up-regulated (Figure 5B, Figure 5D).
Figure 4.
The identification of lipid components.
(A): Clustering heat map of significantly different lipids; (B): identification of lipid components in positive ion mode; and (C): identification of lipid components in negative ion mode. ST: sterol lipids; SP: sphingolipids; GP: glycerophospholipids; GL: glycerolipids; FA: fatty acyls; STEMI: ST-segment elevated myocardial infarction.
Figure 5.
The subclass analysis of significantly different lipids.
(A): Subclass lipid components of positive ion mode; (B): subclass lipid components of negative ion mode; (C): FC of lipids in each lipid subclass detected in untargeted lipidomic profiling of positive ion mode; and (D): FC of lipids in each lipid subclass detected in untargeted lipidomic profiling of negative ion mode. CAR: acylcarnitine; CE: cholesteryl ester; Cer: ceramide; DG: diacylglycerol; DLCL: dilysocardiolipin; FA: free fatty acid; FAHFA: fatty acid ester of hydroxyl fatty acid; HexCer: hexosylceramide; LNAPE: N-acyl-lysophosphatidyl ethanolamine; LPC: lysophophatidylcholine; LPE: lysophosphatidyl ethanolamine; MG: monoacylglycerol; PC: phosphatidylcholine; PE: phosphatidylethanolamine; PI: phosphatidylinositol; PS: phosphatidylserine; SE: sterol esters; SHexCer: sulfatide; SM: sphingomyelin; TG: triacylglycerol; LPI: lysophosphatidylinositol.
Feature Selection Process
Firstly, we used LASSO regression to select lipid molecules. The result showed that, the inclusion of 22 lipid molecules [including CAR (10: 0), CE (17: 0), DG (24: 4), etc. Details were shown in Supplementary Table 1] could obtain the highest efficiency (Figure 6A). Secondly, we used the algorithm of XGboost to calculate the weight of each lipid molecule and rank it. The top five molecules were LPI (18: 2), PC (15: 0/18: 2), LPI (20: 3), SHexCer (35 :2), and CAR (11: 0). The inclusion of the first 10 lipid molecules could obtain the best diagnostic accuracy (Figure 6B). Finally, we selected the overlapping lipid molecules screened by the two feature screening methods as the lipid molecules finally included in the model (Figure 6C), which were PC (15: 0/18: 2), PI (19: 4), LPI (20: 3). All the selected lipid molecules were significantly higher in the STEMI group than the control group (Figure 6D).
Figure 6.
The feature selection process of significantly different lipids.
(A): Feature selection process of LASSO regression; (B): feature selection process using ‘greedy algorithm’ and XGBoost; (C): Venn diagram shows the lipid biomarkers selected by the LASSO and XGBoost algorithms; and (D): differential analysis of selected lipids. AUC: area under the curve; LPI: lysophosphatidylinositol; STEMI: ST-segment elevated myocardial infarction; PC: phosphatidylcholine; PI: phosphatidylinositol.
Model Establishment and Assessment
After selecting three lipid molecules with significant differences between STEMI and control group, we used multivariate logistic regression to establish the lipid model. The result showed that PC (15:0/18:2) (OR: 1.15; 95% CI: 1.04-1.28), PI (19: 4) (OR: 1.38; 95% CI: 1.10-1.74), LPI (20:3) (OR: 2.40; 95% CI: 1.45-3.97) were all independently associated with STEMI (Figure 7A). The ROC analysis showed the lipid model performed well in predicting STEMI (AUC: 0.972; 95% CI: 0.948-0.996) (Figure 7B). The calibration curve showed good agreement between prediction and observation (Figure 7D). The radar chart showed that the diagnostic efficiency of the lipid model for STEMI patients is good in different aspects including sensitivity (86.5%), specificity (96.2%), accuracy (91.3%), NPV (87.7%), and PPV (95.7%) (Figure 7C).
Figure 7.
The diagnostic efficiency of the prediction model.
(A): Forest plot of the lipid model; (B): the ROC curves of the derivation set and validation set; (C): the diagnostic efficiency of logistic model in the derivation set; (D): the calibration curve of lipid model in the derivation set; (E): the calibration curve of logistic model in the validation set; and (F): the diagnostic efficiency of lipid model in the validation set. AUC: area under the curve; CI: confidence interval; NPV: negative predicted value; OR: odds ratio; PPV: positive predicted value; ROC: receiver operating characteristic curve.
The internal validation showed that the model performed excellent in validation set. The ROC analysis showed well discrimination performance in the validation set (AUC: 0.967, 95% CI: 0.958-0.975), while the calibration curve showed good calibration performance in validation set. The sensitivity (87.2%), specificity (95.6%), accuracy (91.4%), NPV (88.2%), and PPV (95.2%) all showed good diagnosis efficacy in the validation set.
Early Diagnosis of STEMI Using Lipid Model
The median time difference between blood collection time and chest pain was 7.5 h. There were 14 patients whose blood collection time was less than 4 h from the onset of symptoms, of which only 7% (one case) patient had an increase in cardiac troponin I (cTnI). There were 38 patients whose blood collection time was more than 4 h from the onset of symptoms, of which six patients (16%) did not have an increase in cTnI (Figure 8A). Using cTnI as STEMI biomarker, the diagnostic accuracy was 63%, while using lipid model, the accuracy was improved to 86.5% (Figure 8B). Nineteen STEMI patients with normal cTnI continued to use the lipid model for diagnosis. The results showed that 95% of patients (18 cases) were accurately diagnosed (Figure 8C).
Figure 8.
The diagnostic efficiency of the prediction model compared with cTnI.
(A): Analysis of the proportion between the time of admission and the increase of cTnI; and (B &C): The accuracy analysis of lipid model STEMI diagnosis. cTnI: cardiac troponin I; STEMI: ST-segment elevated myocardial infarction.
DISCUSSION
At present, STEMI is still one of the most serious and fatal diseases with chest pain as the main complaint. Early and accurate diagnosis is an important way to reduce the mortality and improve the prognosis of STEMI patients. In this study, we performed a comprehensive untargeted lipidomics analysis of STEMI patients. 166 differentially expressed lipid metabolites (mainly were DG, CAR, LPC, LPE, PC, FA, PE, and PI) were identified between the STEMI group and the control group.
After using FC and FDR, which were the most commonly used methods for preliminary variable screening, our study further used two machine learning methods for more accurate variable screening. As we know, the significant feature of LASSO regression model is that it is quite suitable for the case when the number of predictors used in the study is more than the sample of the test object.[14] Moreover, LASSO regression can solve the problem of multicollinearity of independent variables.[15] Therefore, it is especially suitable for the analysis of ‘omics’ data with a large number of independent variables. In this study, we first used LASSO regression to screen the lipid molecules with significant differences, and finally selected 22 best lipid molecule combinations. However, using 22 lipid molecules to predict STEMI are too inconvenient for clinical application. Therefore, we used the second feature selection method to assist in variable screening.
XGBoost is a machine learning algorithm that uses classification and regression trees as weak classifiers.[15] Compared with other algorithms, the XGBoost algorithm allows easy adjustment of parameters and can deal with nonlinear features. It usually has higher sensitivity and specificity when overfitting is avoided. In most cases, XGBoost has higher prediction performance than other algorithms.[16,17] In machine learning, the greedy algorithm is an algorithmic paradigm that follows the problem-solving heuristic of making the locally optimal choice at each stage.[18] Until now, few study used XGBoost combined with greedy algorithm to select variables, which were used by our study to screen high-dimensional data detected by lipidomics. We further selected 10 lipid molecules using XGBoost and greedy algorithm, which were not exactly the same compared with lipids selected by LASSO regression. We creatively used the combination of the above two screening methods to further screen lipid molecules, and obtained three lipid molecules which were PC (15: 0/18: 2), PI (19: 4), and LPI (20: 3). Because the number of lipid molecules with significant difference screened by lipidomics is very large, and the types of lipid molecules with the best predictive value obtained by each screening method are not exactly the same, it may be more valuable to use a combination of multiple methods to screen variables. And in our study, we established the lipid model to predict STEMI using logistic regression, which showed excellent discrimination and validation performance.
In conclusion, there were significant differences between STEMI and patients excluded CAD in lipid metabolism. The differentially expressed lipids were mainly DG, CAR, LPC, LPE, PC, PI, PE, and FA. Using XGBoost combined with greedy algorithm and LASSO regression to screen lipid molecules, and logistic regression to establish prediction model can get a simple and accurate model, which can accurately predict STEMI even in the early stage compared with cardiac troponin.
CONFLICT OF INTERESTS
None.
ACKNOWLEDGMENTS
This work was supported by the National Key Research and Development Program of China (2017YFC0908701), National Natural Science Foundation of China (81972149, 81871850), and Beijing Natural Science Foundation (grant No. 7212125).
Contributor Information
Hai-Yi YU, Email: yuhaiyi@bjmu.edu.cn.
Wei GAO, Email: weigao@bjmu.edu.cn.
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