Early Warning of Ischemic Stroke Based on Atherosclerosis Index Combined With Serum Markers

Abstract

Context

Ischemic stroke (IS) is a serious public health problem worldwide, threatening human life and health. Atherosclerosis is the cause of stroke. At present, there are few selective indexes that can be used to evaluate atherosclerosis in the clinic; providers rely mainly on the atherosclerotic index (AI). Disturbance of lipid metabolism is considered to be a key event leading to IS.

Objective

The purpose of this study was to discover potential biomarkers in the serum of atherosclerosis-induced IS, combined with the AI to provide early warning for the diagnosis of IS.

Methods

In this study, we used nontargeted metabolomics based on ultra-high performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) to measure the changes in serum metabolites in a group of patients with IS. To verify the reproducibility of candidate biomarkers in the population, we expanded the sample size.

Results

Five metabolites were identified, including sphingomyelin (18:0/14:0), 1-Methylpyrrolinium, PC (18:0/18:0), LysoPC (18:0/0:0), and PC (18: 2/18:2). The combination of these 5 metabolic markers has good diagnostic and predictive ability, and the change level of these metabolites is significantly related to IS. Our results also indicate that changes in glycerophospholipid metabolism may indicate an early risk of IS development.

Conclusion

These findings may contribute to the development of new diagnostic methods of potential biomarkers in serum combined with the AI, thereby providing early warning for the diagnosis of atherosclerosis-induced IS, and may provide a new insights for pathogenesis in IS.

Stroke is an acute cerebral blood circulation disorder caused by various predisposing factors that cause stenosis, occlusion, or rupture of intracerebral arteries (1). Stroke is currently one of the major diseases threatening global human health and has become the second leading cause of death in the world (2). Stroke is divided into ischemic stroke (IS) and hemorrhagic stroke, and the incidence of IS is dominant (3). IS refers to a localized ischemic necrosis of brain tissue or encephalomalacia caused by blood supply obstacles to the brain, ischemia, and hypoxia (4, 5). IS mainly originates from atherosclerosis, and about one-third of patients with IS have atherosclerosis (6, 7). The occurrence and development of atherosclerosis is a slow and gradual process, usually for several years or decades (8). However, complications of atherosclerosis occur suddenly, usually without warning (9, 10). Therefore, it is necessary to find blood metabolites as a predictor of stroke induced by atherosclerosis.

Atherosclerosis and IS are closely related. However, the early warning of atherosclerosis has limitations, mainly relying on the atherosclerosis index (AI). In recent years, some scholars have defined the concept of AI as [serum total cholesterol-serum high density lipoprotein cholesterol]/serum high density lipoprotein cholesterol (11-13). It has been reported in the literature that AI is positively correlated with the severity of atherosclerosis (14). Its normal value is that the AI is less than 4, if the atherosclerosis index (AI) is less than 4, it reflects that the degree of arteriosclerosis is not serious or is reducing, and the risk of cardiovascular and cerebrovascular diseases is lower. In contrast, the risk of developing cardiovascular and cerebrovascular diseases is higher (13). Therefore, we group the subjects by AI.

Metabolisms (or metabonomics) is a discipline that developed after proteomics and genomics (15). It observes the dynamic changes of metabolites before and after the biological system is stimulated or disturbed, and uses various analytical tools to obtain the metabolic network of the biological system (16). Metabolisms has been widely used in various scientific fields because of its high access, integrity, high sensitivity, and high selectivity (17). In the context of systems biology, metabolisms uses qualitative and quantitative analysis of the endogenous metabolite changes of various organisms to evaluate the pathophysiological stimulation of the body, which is an important analysis method for the study of clinical metabolism (18).

Therefore, this experiment uses metabolisms analysis technology to perform nontargeted metabolisms detection and analysis on patient serum. Four groups have been defined: AI less than 4, AI greater than 4, IS with AI less than 4, and IS with AI greater than 4. We aimed to identify biomarkers combined with AI to predict atherosclerosis-induced IS; explore the influence of markers on metabolic pathways and their role in the occurrence and development of IS; and provide a basis for the prevention and precise treatment of atherosclerosis-induced IS.

Methods and Methods

Ethics Approval

Approval from the local ethics committee was obtained before the onset of the study, and all the participants provided their consent before the study began. This study was approved by each participant and the ethics committee of the First Affiliated Hospital of Tianjin University of Traditional Chinese Medicine (ethics No. TYLL2016[K]No. 001). All participants provided informed consent, and all procedures conformed to the ethical guidelines of the 1975 Declaration of Helsinki.

Instrument and Reagents

The following instruments and reagents were used: distilled water (Guangzhou Watsons Food & Beverage Co Ltd), chromatographic pure acetonitrile (Oceanpak Co), chromatographic pure formic acid (ROE Co), low-temperature high-speed centrifuge (Changsha Xiangyi Centrifuge Instrument Co Ltd), JKYMEN ultrasonic cleaner, small oscillation vortex (Haimen Qilin Bell Instrument Manufacturing Co Ltd), Waters Acquity UPLC Liquid Chromatograph (Waters Co), Waters Xevo G2S Q-TOF Mass Spectrometer (Waters Co), ACQUITY UPLC BEH C18 Chromatography Column (2.1 × 100 mm, 1.7 μm, Waters Corp).

Study Design and Participants

A total of 400 individuals participated in this study, and these were divided into 4 groups: those with an AI less than 4 (L), AI greater than 4 (G) (group G is the control group of this study), IS AI less than 4 (SL), and IS AI greater than 4 (SG). Serum samples were collected from August 2019 to December 2020. Blood was collected from fasting patients the first morning in the hospital, and samples meeting the inclusion and exclusion criteria of this study were collected, forming the 2 stages of discovery and verification in this study. In the discovery phase, 160 samples were collected to form 4 groups of L, G, SL, and SG; in the verification phase, 240 samples were collected to form 3 groups of L, G, and SG.

The inclusion criteria for individuals with IS are as follows: (1) aged between 18 and 80 years, who received a definite diagnosis in our hospital and met the diagnostic criteria for IS; (2) all examination items and physical signs were complete; (3) good nutritional status; (4) clear consciousness, no intellectual disability, and able to communicate normally.

Exclusion criteria for individuals with IS included the following: (1) complications with other brain diseases, menopausal syndrome, hyperthyroidism, myelopathy or vertebral artery cervical spondylitis, gastroesophageal reflux disease or diseases that may cause chest pain, such as hiatal hernia; (2) combined with severe cardiopulmonary insufficiency or severe arrhythmia (eg, rapid atrial fibrillation, atrial flutter, paroxysmal ventricular tachycardia); (3) abnormal renal function; (4) combined with serious primary diseases such as hematopoietic system or malignant tumors; (5) pregnant women, lactating women, or women of childbearing age who will undergo childbirth; (6) individuals with mental illness or cognitive dysfunction; (7) patients with diabetes, gouty nephropathy, and other diseases that may affect the results of this experiment.

All participants met the inclusion and exclusion criteria specified in this study. The clinical diagnosis of IS is based on the history, physical, and neurological examinations supplemented by neurovascular imaging data or computed tomography, magnetic resonance imaging, ultrasound, and/or angiography. The diagnostic criteria refer to the early management guidelines for patients with acute IC developed by the American Heart Association and the American Stroke Association in 2013 (19).

We used SPSS version 26.0 for analysis of clinical sex and age data (Kruskal-Wallis H test), and the results showed that for sex (discovery set, P = .995; validation set, P ≥ .999) and age (discovery set, P = .399; validation set P = .202,), there was no statistical significance in sex and age among the groups (P > .05). Detailed participant information is listed in Table 1, and the research design is shown in Fig. 1.

Table 1.

Participant demographic and clinical information

Clinical characteristics
	No.	Sex, Male/Female	Age, y	TC	HDL-C	AI
Discovery set
L	40	20/20	60.35 ± 4.96	5.54 ± 0.98	1.40 ± 0.28	2.49 ± 0.65
G	40	20/20	60.9 ± 9.64	5.86 ± 0.94b	1.04 ± 0.18b	4.66 ± 0.53b
SL	40	20/20	61.05 ± 10.84	5.43 ± 0.84a	1.23 ± 0.12a	3.04 ± 0.94a
SG	40	20/20	60.02 ± 11.13	5.76 ± 1.76a	0.98 ± 0.05b	5.62 ± 1.94b
Validation set
L	80	50/30	60.53 ± 8.96	4.65 ± 0.71	1.34 ± 0.27	2.39 ± 0.76
G	80	50/30	60.89 ± 10.64	5.64 ± 0.63a	1.02 ± 0.12b	4.76 ± 0.62b
SG	80	50/30	60.71 ± 10.12	5.94 ± 1.26a	0.95 ± 0.14b	5.72 ± 0.94b

Data are presented as mean SD.

Abbreviations: AI, atherosclerosis index; G, AI greater than 4; HDL-C, high-density lipoprotein cholesterol; L, AI less than 4; SG, AI of ischemic stroke greater than 4; SL, AI of ischemic stroke less than 4; TC, total blood cholesterol.

^a P less than .05.

^b P less than .01, disease group compared to healthy control (AI < 4).

Figure 1.

Design of the study.

Serum Sample Collection and Processing

Samples were stored in a refrigerator at –80 °C immediately after collection and thawed in a 4 °C environment before analysis. A total of 50 μL of serum was aspirated, 150 μL of acetonitrile was added. After ultrasonic treatment in an ice-water bath for 10 minutes, vortexed and mixed for 1 minute, centrifuged at 13 000 rpm at 4°C for 15 minutes, the supernatant removed, and awaited UPLC-Q-TOF/MS analysis. The pretreatment of the quality control (QC) samples was parallel and the same as the treatment of the research samples. QC samples were evenly inserted into each set of analysis run sequences to monitor the stability of large-scale analysis.

Ultra–High-Performance Liquid Chromatography–Quadrupole Time-of-Flight Mass Spectrometry Analysis

This study was carried out using a Waters Acquity UPLC liquid chromatograph (Waters Co) and Waters Xevo G2S Q-TOF mass spectrometer (Waters Co). We used an ACQUITY UPLC BEH C18 column (2.1 × 100 mm, 1.7 μm; Waters) in ESI⁺ mode. Detailed experimental conditions for UPLC separation and MS detection are described next.

Chromatographic analysis conditions included the following: column temperature: 45 °C; flow rate: 0.3 mL/min; injection volume: 5 μL. Mobile phase composition: A: 0.1% formic acid water and B: 0.1% formic acid acetonitrile. The elution gradient is 0 to 0.5 minutes, 1%B; 0.5to 2 minutes, 1% to 50%B; 2 to 9 minutes, 50% to 99%B; 9 to 10 minutes, 99%B; 10 to 10.5 minutes, 99% to 1%B; 10.5 to 12 minutes, 1%B.

MS analysis conditions included the following: capillary voltage: 2.0 kV, ionization source temperature: 100 °C, dry gas flow rate: 10 mL/min, solvent removal flow rate: 600L/D, solvent removal temperature: 450 °C, cone air flow rate: 50 L/D, and quadrupole scanning range m/z: 50-1000.

Data Analysis

Data export and processing procedures included the following: the MassLynx V4.1 Workbench (Waters) converted the separately collected groups of liquid quality information into data files. We used the Wukong platform (20)(https://www.omicsolution.com/wkomics/main/) to round off the data by 80%, and used the k-nearest neighbor method (the number of nearest neighbors is 5) to fill in missing values to achieve data preprocessing (21). The data processing process has good reproducibility. SIMCA-P 14.1 (Umetrics AB) was used for multivariate analysis. Unsupervised principal component analysis (PCA) was used to assess the overall metabolism changes between groups and monitor the stability of the study. A supervised model of partial least squares discriminant analysis (PLS-DA) was carried out to maximize the distance between groups and identify important variables that have important contributions to classification according to their important variables in the projection (VIP). We then conducted 200 permutation tests to assess the risk of model overfitting.

Statistical analysis and identification of data included the following: the data were subjected to normality test and analysis of variance using SPSS 26.0 statistical software, followed by t test (for normal data) and Kruskal-Wallis H test (for nonnormal data) for between-group differences. Differential markers were assessed for significant disease-related changes (P < .05). The HMDB database (http://www.hmdb.ca/) was searched according to the m/z values of the differential markers, and candidate biomarkers were determined by MS/MS fragment information. Subsequently, candidate biomarkers were validated using the validation set subject data and the validation results were subjected to multiple comparison analysis.

Data visualization and functional analysis included the following: Binary logistic regression was used to build models based on potential biomarkers. The receiver operating characteristic (ROC) curve was used to evaluate the results of the regression analysis. To show the results more clearly, Metaboloanalyst (version 4.6.0) and GraphPad Prism (version 8) were used to draw heat maps and scatter plots of potential metabolic markers to express the results of correlation analysis. At the same time, pathway analysis based on differential metabolites revealed that important metabolic pathways are disturbed.

Results

Demographics of the Study Population

The work flowchart of the study is shown in Fig. 1. To define candidate markers, 160 serum samples were collected in the discovery group. A total of 240 participants were recruited to validate these candidate biomarkers and define potential biomarkers in the validation set. Table 1 list the clinical information of all the participants.

Serum Metabolic Profile

Supplementary Fig. S1 (22) shows the QC ion chromatograms extracted in the ES+ mode of the discovery set and the verification set. First, we performed phenotypic analysis on the data. In the PCA score graphs of the discovery set and the validation set (Supplementary Fig. S2A and S3B) (22), the QC samples are closely clustered, which further confirms the reliability of this study. In addition, the differentiation trend between samples from the L, G, SL, and SG groups indicates there are statistically significant systemic metabolic differences between these groups. These variables were used for subsequent multivariate and univariate analysis.

Definition of Potential Metabolic Biomarkers of Ischemic Stroke Induced by Atherosclerosis

We first established the set PLS-DA score map (Fig. 2A-2C), revealing the obvious separation between these groups, and in the PLS-DA model of this study, the R²X of the L and G groups = 0.883, Q² = 0.776; R²X = 0.849, Q² = 0.721 in the G and SG groups; and R²X = 0.906, Q² = 0.772 in the L and SL groups, indicating that the prediction model established in this experiment had a good fit (Fig. 2D-2F). The test results are stable and reliable. Based on the results given by the PLS-DA model, further screening of the metabolites of 17 with VIP greater than 1.0 on the 2 main components (Supplementary Fig. 2C) (22) helped determine the potential metabolism of important variables for atherosclerosis-induced IS biomarkers.

Figure 2.

A, L vs G PLS-DA analysis; B, G vs SG PLS-DA analysis; C, L vs SL PLS-DA analysis; D, L vs G permutation test; E, G vs SG permutation test; F, L vs SL permutation test. G, atherosclerosis index greater than 4; L, atherosclerosis index less than 4; SG, ischemic stroke atherosclerosis index greater than 4; PLS-DA, partial least squares discriminant analysis; SL, ischemic stroke atherosclerosis index less than 4.

Using the intersection data of L and SL groups in 17 metabolites to exclude metabolic biomarkers that may affect atherosclerosis-induced IS, these markers were subsequently tested for normality and analysis of variance using SPSS 26.0; a statistically significant change (P < .05) of disease-related differential markers was obtained. Using the m/z values of the differential metabolites, the HMDB database was used to screen and compare the differential metabolites. After the aforementioned analysis, 9 candidate biomarkers were found (Table 2). After verification, 5 potential biomarkers were finally obtained. Subsequently, the multiple comparison analysis of the 5 potential biomarkers was performed using the participant data of the validation set. The results showed that the P values of the 5 potential biomarkers between the G and SG groups were all less than .05, and the difference between the groups was statistically significant.

Table 2.

Candidate biomarker information

No.	tR/min	m/z		ppm	Metabolites	Formula	Adduct format	Discovery set (SG/G)
		Obsd.	Calcd					Variance	Fold change
1	0.76	148.9352	148.9328	16.11	2,2,2-Trichloroethanol	C2H3Cl3O	M + H	↑b	1.14
2	0.91	84.0814	84.0813	1.19	1-Methylpyrrolinium	C5H10N	M + H	↓a	0.67
3	0.92	148.0431	148.0432	0.68	Thiomorpholine 3-carboxylate	C5H9NO2S	M + H	↑b	1.46
4	2.44	171.1129	171.115	12.27	2,4-Dimethyl-1-(1-methylethyl)-benzene	C11H16	M + Na	↓a	0.73
5	4.96	715.5158	715.5156	0.28	SM (18:0/14:0)	C37H77N2O6P	M + K	↓b	0.64
6	6.4	508.3767	508.3767	0	LysoPC (18:0/0:0)	C26H54NO6P	M + H	↑b	1.66
7	8.83	814.6362	814.6326	4.42	PE-NMe (18:1/22:1)	C46H88NO8P	M + H	↑b	1.42
8	9.62	812.6004	812.5935	8.49	PC (18:0/18:0)	C44H88NO7P	M + K	↓a	0.68
9	11.2	804.5509	804.5519	1.24	PC (18:2/18:2)	C44H80NO8P	M + Na	↑b	2.97

Abbreviations: G, atherosclerosis index greater than 4; SG, atherosclerosis index of ischemic stroke greater than 4; SM, sphingomyelin.

^a Significant difference P less than .05.

^b Very significant difference P less than .01.

Metabolic Marker Analysis

According to the previously described methodologies data processing results (see Table 2), it can be seen that 9 biomarkers were screened in the discovery collection. Compared with G in the discovery collection samples, the levels of 9 metabolites in IS patients were significantly changed. Among them, 4 metabolic markers of SM (18:0/14:0), 2,4-dimethyl-1-(1-methylethyl)-benzene, 1-methylpyrrolinium, and PC (18:0/18:0) were compared with those of the control group, and found to be decreased significantly. LysoPC (18:0/0:0); thiomorpholine 3-carboxylate; 2,2,2-trichloroethanol; PC (18:2/18:2); and PE-NMe (18:1/22:1) comprised the 5 metabolism markers that increased significantly compared with the control group. Among the 5 biomarkers confirmed by the validation set, 3 metabolic markers of SM (18:0/14:0), 1-methylpyrrolinium, and PC (18:0/18:0) were significantly lower than those of the control group. Two metabolic markers, LysoPC (18:0/0:0) and PC (18:2/18:2), increased significantly compared with the control group. Most of the differential biomarkers found are lipid substances, and when screening candidate biomarkers for atherosclerosis-induced IS, the difference markers obtained after the intersection of the L group and the SL group were compared with the 17 substances and found to have no intersection.

This study has found potential biomarkers that may be used to diagnose arteriosclerosis-induced IS. To further explore whether these markers have diagnostic significance, this study used ROC curves to verify the diagnostic ability of potential biomarkers and judgment. In addition, Net Reclassification Improvement and Integrated Discriminant Improvement Index analyses were performed to assess the predictability of the combined diagnosis of the 5 potential biomarkers. Metabolite information of the 5 potential biomarkers screened out was used to establish a binary logistic regression model and ROC curve. Finally, a combined diagnosis model of the 5 markers was obtained (Fig. 3A and 3B), The results showed that the algorithm combining 5 specific markers could accurately distinguish SG from G with an area under the curve of 0.841 (95% CI, 0.750-0.932), sensitivity of 80%, specificity of 82.5%, and positive predictive value of 80.0%. The negative predictive value is 82.5%, and the result is also validated in the validation set (Table 3), in the process of data analysis of discovery set and validation set, with a single potential compared with biomarkers, the addition of 5 potential biomarkers predicted probability value, Net Reclassification Improvement, and Integrated Discriminant Improvement Index analysis P less than .001, which was statistically significant, showing that the combination of 5 metabolic markers has a better diagnostic prediction ability.

Figure 3.

A, Receiver operating characteristic (ROC) curve result of the discovery set. B, ROC curve result of the verification set. C: Heat map analysis of 5 potential biomarkers.

Table 3.

Evaluation results of the marker combination diagnostic model

	AUC (95%CI)	P	Cutoff	Sen, %	Spe, %	PPV, %	NPV, %
Discovery set	0.841 (0.750-0.932)	< .001	0.466	80.0	82.5	80.0	82.5
Validation set	0.787 (0.718-0.856)	< .001	0.357	88.8	52.5	65.1	82.4

Abbreviations: AUC, area under the curve; NPV, negative predictive value; PPV, positive predictive value; Sen, sensitivity; Spe, specificity.

Relationship Between Potential Biomarkers and Clinical Features and Ischemic Stroke Induced by Atherosclerosis Index Greater Than 4

Pathway analysis of the 5 potential biomarkers (Fig. 4A) showed that the metabolic disorder caused by IS induced by an AI greater than 4 is mainly related to the metabolism of glycerophospholipids, and also affects the metabolism of linoleum acid, α-linoleum acid (ALA) metabolism, SM, anachronic acid metabolism, and other pathways.

Figure 4.

A, Validation set metabolic marker pathway analysis diagram and scatter plot analysis of 5 potential biomarkers. B to F, Scatter plot of 5 potential biomarkers.

To observe the relative level changes of biomarkers in different groups more intuitively, we used hierarchical cluster analysis—heat maps and scatter plots to analyze key biomarkers to distinguish them. The heat map shows the significant changes of the 5 metabolic markers obtained in the validation set among the 3 groups, and the trend of change is shown in Fig. 3C. Through the scatter plot (Fig. 4B-4F), we found that there are significant differences between the control group and the other 2 groups in the 5 markers.

To further reveal the relationship between these markers and the AI, the markers and AI were jointly analyzed. Among them, 1-methylpyrrolinium, LysoPC (18:0/0:0), PC (18:0/18:0), and the AI are closely related. The study found that in patients with an AI greater than 4, when 1-methylpyrrolinium, SM (18:0/14:0), and PC (18:0/18:0) show a downward trend, LysoPC (18:0/0:0) and PC (18:2/18:2) show an upward trend, indicating that this population is at high risk of atherosclerosis-induced IS. At the same time, the discovery of markers can provide a theoretical basis for the diagnosis of IS induced by atherosclerosis. In summary, combined with the AI, 5 potential biomarkers may provide early warning for the diagnosis of atherosclerosis-induced IS. These findings may contribute to the development of new diagnostic methods of potential biomarkers in serum combined with the AI.

Discussion

IS is a serious public health problem worldwide that seriously threatens human life and health. Unfortunately, current clinical imaging of IS may lead to misdiagnosis or missed diagnosis. Therefore, the development of new biomarker-based detection methods is still needed (23). At present, the identification of new potential serum biomarkers for the detection of atherosclerosis-induced IS is still a vital goal, especially for the diagnosis of early IS. However, owing to the limited research cohorts or diagnostic performance, there is still a gap in the conversion of biomarker candidates into clinical applications.

Metabolism is considered to be a key event leading to IS. In this study, a total of 400 individuals were recruited to analyze the metabolites in their serum. The differential metabolites were identified by PCA and PLS-DA analysis, and biomarkers were identified using the HMDB, MetPA, and KEGG databases. Seventeen potential biomarkers that had a significant effect on clustering were finally located. We paid attention to the changes in metabolic pathways related to biomarkers and found that these metabolic pathways are related to the clinical symptoms of atherosclerosis-induced IS. The details of the relevant metabolic pathway network are shown in Supplementary Fig S3 (22).

Glycerol phospholipid metabolism is one of the metabolic pathways with abnormal metabolic levels in IS. The occurrence of IS causes a certain degree of cerebral circulation disorder, but the cerebral circulation disorder leads to the destruction of phospholipid metabolism. Phosphate-containing lipids in glycerophospholipid metabolism are called phospholipids, which are widely distributed in various tissues and cells of the body. They are not only important components of biological membrane structure and plasma lipoproteins. In recent years, phospholipids have also been found to play a very important role in cell recognition and signal transduction (24). Phospholipids mainly include glycerophospholipids and SM. Lipid peroxidation and activation of protein kinase C and the release of intracellular calcium lead to the destruction of phosphatidylcholine homeostasis. Use of cytidine-5-diphosphate choline is used as an intermediate compound in the biosynthesis of cell membrane phospholipids, helping to stabilize the cell membrane and reduce the formation of free radicals (25). In this study we found that the oxidative stress level in the body is changed after the level of glycerophospholipids is reduced under disease conditions. Oxidative stress in the blood vessel wall can cause the oxidative modification of low-density lipoprotein (LDL), which leads to the oxidative modification of LDL, producing oxidized LDL (ox-LDL). After endothelial cells take up ox-LDL, the lipid composition of ox-LDL can damage vascular endothelial cells and their functions by reducing vasodilation, inducing thrombosis and inflammation (26).

The pathway that affects the abnormal level of metabolism in IS also includes the metabolism of linoleum acid. Linnaeus acid in linoleum acid metabolism is recognized as an essential fatty acid. Because linoleum acid can lower blood cholesterol and prevent atherosclerosis, it is highly valued (27). Studies have found that cholesterol must be combined with linoleum acid before it can function and metabolize normally in the body. If there is a lack of linoleum acid, cholesterol will combine with some saturated fatty acids, causing metabolic disorders, depositing on the blood vessel wall, and gradually forming atherosclerosis, leading to cardiovascular and cerebrovascular diseases (28). Linnaeus acid lowers blood lipids, softens blood vessels, lowers blood pressure, and promotes microcirculation. It can prevent or reduce the incidence of cardiovascular diseases, especially for high blood pressure, hyperlipidemia, angina pectoralis, coronary heart disease, atherosclerosis, and in older populations. The prevention and treatment of obesity are extremely beneficial. It can prevent the deposition of human serum cholesterol on the blood vessel wall, and has the health care effect of preventing atherosclerosis and cardiovascular disease (29).

It has been confirmed that ALA metabolism can also affect the metabolic level of ischemic stroke. ALA is a polyunsaturated fatty acid (PUFA) with 3 double bonds. It is an omega-3 essential fatty acid used to improve intelligence and fight blood clots. The physiological effects of ALA can inhibit thrombotic diseases and prevent myocardial infarction and cerebral infarction (30). The hypoglycemic effect of ALA is achieved on the one hand by regulating the metabolic rate, and on the other hand by inhibiting related fat and glycerol synthase and cholesterol synthase. ALA can reduce the activity of the rate-limiting enzyme HMG-CoA of cholesterol synthetase and reduce the production of cholesterol; α-linolenic acid has an effect on fat synthetase (including fatty acid synthase, CoA-carboxylase, diacylglycerol acetyl, transferase, etc), inhibiting and strengthening β-oxidation in mitochondria, reducing triglyceride synthesis and increasing consumption (31). ALA inhibits the metabolism of ω-6 PUFA through competitive inhibition, reduces the synthesis of prostaglandin PGE2, prostaglandin PGI2, etc, and increases the corresponding metabolites of ω-3 PUFA, thereby producing numerous biological regulation effects, such as anti-inflammatory and antithrombotic effects (32). At the same time, hyperlipidemia is an important risk factor for the formation and progression of atherosclerotic lesions. It has been confirmed that lipid-lowering drugs can delay the occurrence of atherosclerotic events (such as myocardial infarction and stroke). Many experiments have concluded that ALA can reduce serum total cholesterol, triglycerides, LDL, and very low-density protein, and increase serum high-density lipoprotein (33).

The experimental results showed that the content of sphingomyelin (SM [18:0/14:0]) in the serum of the SG group decreased. SM metabolites are an important component of cell membranes and can participate in various biological processes such as cell growth, differentiation, aging, and apoptosis (34). The basic process of the synthesis and metabolic pathway of SM metabolites in the human body includes serine and Palmityl-CoA production of dihydrosphingosine, which can be produced by combining with fatty acyl groups. Ceramide can be further phosphorescent or glycerogelatin to produce SM, or sphingosine can be produced under the action of hydrolase. Dihydrosphingosine plays an important role in the metabolism of sphingomyelin and is the basic unit for the synthesis of various complex sphingolipid compounds (35). In this study, a significant decrease in the content of sphingomyelin caused abnormal metabolism of sphingomyelin, which may induce cell necrosis and apoptosis, thereby triggering IS.

There are some limitations to this study. For example, this experiment was a cross-sectional study and there is insufficient evidence that all participants experienced atherosclerotic-induced IS. In addition, owing to the limitations of the clinical information collection methods from the samples, the demographic information of the samples could not be presented as much as possible, and related issues should be improved in future research. Metabolomics has great potential in discovering novel biomarkers of stroke and identifying IS, but further studies are needed to elucidate the underlying pathways and mechanisms of these associations and further investigate their potential clinical applications.

In short, through the study of large samples, the potential biomarkers of IS induced by atherosclerosis have been defined and verified. Biomarkers can help us effectively identify IS induced by atherosclerosis. In addition, metabolomics showed certain advantages in screening markers. Through the marker analysis of this study, it was found that the proportion of lipids in the markers of atherosclerosis-induced IS is relatively large. Therefore, the combined analysis of metabolism and liposome can be considered to more accurately discover potential biomarkers of the disease.

Glossary

Abbreviations

AI

atherosclerosis index

ALA

α-linoleum acid

G

atherosclerosis index greater than 4

IS

ischemic stroke

L

atherosclerosis index less than 4

LDL

low-density lipoprotein

ox-LDL

oxidized low-density lipoprotein

PCA

principal component analysis

PLS-DA

partial least squares discrimination analysis

PUFA

polyunsaturated fatty acid

SG

atherosclerosis index of ischemic stroke greater than 4

SL

atherosclerosis index of ischemic stroke less than 4

SM

sphingomyelin

QC

quality control

UPLC-Q/TOF-MS

ultra–high-performance liquid chromatography–quadrupole time-of-flight mass spectrometry

VIP

variable important in the projection

Financial Support

This work was supported by the High-level Innovation Team of Tianjin Special Development Support Program-Metabolomics and New Drug Discovery of Traditional Chinese Medicine (No. KZ1901), and the Science and Technology Talent Cultivation Project of Tianjin Municipal Health Commission (No. KJ20053).

Author Contributions

Conception and design: J. Li, L. Song, Y. Li, and S. Yang; experimental operation: W. Zhou and S. Li; data analysis and writing: W. Zhou and S. Li.; technical or material support: G. Sun, W. Feng, S. Chen, Y. Dong, R. Li, and H. Liu.

Disclosures

The authors do not have any possible conflicts of interest.

Data Availability

Data generated or analyzed during this study are included in this published article or in the data repositories listed in “References.” Some data sets generated during and/or analyzed during the present study are not publicly accessible but are available from the corresponding author on reasonable request.