Use of phosphatidylinositol and phosphatidylglycerol as metabolic markers for prediction of intracranial atherosclerosis progression

Abstract

The application relates to application of phosphatidylinositol and phosphatidylglycerol as metabolic markers for predicting intracranial atherosclerosis progression, and belongs to the technical field of medical biological detection. In the scheme, single-sample MR analysis is carried out by combining RICAS cohort data with external database data, causal correlation between metabolites and ICAS is analyzed, and serum biomarkers phosphatidylinositol PI 18:0 / 22:6 and phosphatidylglycerol PG 18:1 / 18:2 for ICAS occurrence and development are screened out by combining characteristic metabolic changes of ICAS with causal correlation analysis. ICAS progression is collected through prospective follow-up of the RICAS cohort, the correlation between the serum biomarkers and the ICAS progression is established, and it is verified that the serum biomarkers have transformation application value and can be used as a new biomarker for early prediction of ICAS progression and derivation.

Description

TECHNICAL FIELD

[0001] The application relates to the application of phosphatidylinositol and phosphatidylglycerol as a metabolic marker for predicting intracranial atherosclerosis progression, and belongs to the technical field of medical biological detection. BACKGROUND

[0002] Intracranial atherosclerosis (ICAS) is prevalent in the elderly population and is one of the most common causes of stroke worldwide, bringing the highest risk of recurrent stroke compared to other causes of stroke, and causing a heavy disease burden. ICAS usually undergoes a symptomless evolution process for several years or even decades before clinical symptoms such as stroke appear. Different individuals have different ICAS progression patterns. Some ICAS shows rapid progression of stenosis, and repeated adverse clinical outcomes such as stroke still occur under optimal drug treatment. Some ICAS shows a relatively stable state and does not show significant progression of stenosis for decades or even more than a decade.

[0003] Different progression patterns of ICAS are closely related to different outcomes of patients. Early dynamic monitoring of ICAS progression and screening of high-risk individuals are of great significance for early prevention and treatment of stroke. However, the early progression of ICAS is hidden, and the screening rate is low. Early identification of high-risk individuals is still a major clinical challenge.

[0004] Blood lipid disorders, such as elevated low-density lipoprotein cholesterol (LDL-C) and reduced high-density lipoprotein cholesterol (HDL-C), are considered to be the main driving factors for the occurrence and development of ICAS. However, despite lipid-lowering treatment and other controllable risk factor interventions based on evidence-based medicine, there is still a significant residual stroke risk in the ICAS population. Clinically, there is a need to further discover new controllable risk factors to assist in the primary and secondary prevention of ICAS-related stroke.

[0005] The revolutionary progress of omics technology has greatly promoted the development of risk prediction of atherosclerotic cardiovascular disease (ASCVD). Plasma lipidomics and metabolomics analysis provides a large amount of information on the expression levels of lipid metabolism molecules in addition to conventional lipid parameters, providing more abundant information on lipid disorders, making individual risk stratification and precision medicine diagnosis and treatment possible. Previous lipidomics and metabolomics studies of peripheral atherosclerosis have shown that glycerolipids, sphingolipids, and fatty acyls are potential therapeutic intervention targets for peripheral atherosclerosis, and have shown good clinical translation application prospects in preliminary experimental studies.

[0006] Due to the unique anatomical features (thin vessel wall) and atherosclerotic pathophysiological evolution process of ICAS, ICAS may also have characteristic metabolic and lipidomic characteristics. The lipid and metabolic profile of ICAS is worth further exploring, but the mechanism between lipid metabolites and ICAS progression has not been elucidated. SUMMARY

[0007] In order to solve the above problems, the application of phosphatidylinositol and phosphatidylglycerol as metabolic markers for predicting the progression of intracranial atherosclerosis is provided, in order to systematically characterize the ICAS metabolomics characteristics and elucidate the mechanism of metabolites in disease progression, the baseline population of the RICAS cohort (Rose asymptomatic IntraCranial Artery Stenosi Explore, Shandong Pingyin County Kongcun Town Asymptomatic Intracranial Atherosclerosis Cohort) is used as the discovery set and the verification set, a comprehensive lipid and metabolic analysis of ICAS is performed, and the metabolic changes of ICAS are obtained; Mendelian randomization (MR) analysis is performed combined with gene data to infer the causal relationship between lipid metabolites and ICAS; combined with the characteristic metabolic changes of ICAS and causal relationship analysis, the serum biomarkers of ICAS occurrence and development are screened; further, the ICAS progression is collected through the prospective follow-up of the RICAS cohort, and the predictive effect of the markers on the progression of ICAS is verified, which has a value of transformation application.

[0008] The application provides the application of the biomarkers phosphatidylinositol and phosphatidylglycerol in preparing a product for predicting the progression of intracranial atherosclerosis, characterized in that the phosphatidylinositol is PI 18:0 / 22:6, and the phosphatidylglycerol is PG 18:1 / 18:2.

[0009] Optionally, the sample in the progression prediction process is one or more of serum, plasma and blood.

[0010] The application provides the application of the reagent for detecting the content of the biomarkers phosphatidylinositol and phosphatidylglycerol in preparing a product for predicting the progression of intracranial atherosclerosis, characterized in that the phosphatidylinositol is PI 18:0 / 22:6, and the phosphatidylglycerol is PG 18:1 / 18:2.

[0011] Optionally, the sample in the progression prediction process is one or more of serum, plasma and blood.

[0012] The application provides a test agent for predicting progression of intracranial atherosclerosis, characterized in that the test agent comprises components for specifically detecting the contents of biomarkers phosphatidylinositol and phosphatidylglycerol, the phosphatidylinositol being PI 18:0 / 22:6, and the phosphatidylglycerol being PG 18:1 / 18:2.

[0013] The application provides a test kit for predicting progression of intracranial atherosclerosis, characterized in that the test kit comprises the test agent described above.

[0014] The application provides an application of biomarkers phosphatidylinositol and phosphatidylglycerol in screening drugs for preventing intracranial atherosclerosis, characterized in that the phosphatidylinositol is PI 18:0 / 22:6, and the phosphatidylglycerol is PG 18:1 / 18:2.

[0015] The application provides a prediction model for predicting progression of intracranial atherosclerosis, characterized in that the prediction model comprises the following identification and judgment formula:

[0016] P 预测 =-1.7812-0.5035*PI 18:0 / 22:6-0.3814*PG 18:1 / 18:2+0.3654*whether blood lipids are abnormal+0.0099*age-0.5788*gender+0.5805*whether there is diabetes.

[0017] Wherein, PI 18:0 / 22:6 and PG 18:1 / 18:2 are the contents of phosphatidylinositol and phosphatidylglycerol, respectively, which are obtained by z-scale transformation after correction by an internal standard; whether blood lipids are abnormal and whether there is diabetes are 1, and 0 is for no; for gender, 1 is for male, and 2 is for female; when P 预测 is greater than 0.268, it is considered that ICAS progression will occur subsequently, and when P 预测 is less than 0.268, it is considered that ICAS progression will not occur subsequently; or,

[0018] The prediction model comprises the following identification and judgment formula:

[0019] P 预测 =-1.6681-0.3501*PI 18:0 / 22:6-0.1219*PG 18:1 / 18:2+0.0531*whether blood lipids are abnormal+0.0097*age-0.604*gender+0.7644*whether there is diabetes.

[0020] Wherein, PI 18:0 / 22:6 and PG 18:1 / 18:2 are the contents of phosphatidylinositol and phosphatidylglycerol respectively, which are obtained by z-scale conversion after correction by internal standard; for whether blood lipid abnormality and whether diabetes, it is 1, and 0 for no; for gender, male is 1, and female is 2; when P 预测 greater than 0.277, it is considered that ICAS progression will occur subsequently, and when P 预测 less than 0.277, it is considered that ICAS progression will not occur subsequently.

[0021] The application provides an electronic device, characterized by comprising:

[0022] A processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to execute the prediction model for predicting intracranial atherosclerosis progression described above.

[0023] The application provides a readable storage medium, characterized in that when the instructions in the storage medium are executed by the processor of an electronic device, the electronic device can execute the prediction model for predicting intracranial atherosclerosis progression described above.

[0024] The application has the following beneficial effects, but is not limited to:

[0025] In the application scheme, single-sample MR analysis is performed by using RICAS cohort data combined with external database data, causal correlation between lipid metabolites and ICAS is analyzed, and serum biomarkers phosphatidylinositol PI 18:0 / 22:6 and phosphatidylglycerol PG 18:1 / 18:2 for ICAS occurrence and development are screened and obtained by combining ICAS characteristic metabolic changes and causal correlation analysis, ICAS progression is collected by prospective follow-up of the RICAS cohort, and the correlation between phosphatidylinositol PI 18:0 / 22:6 and phosphatidylglycerol PG 18:1 / 18:2 and ICAS progression is established, and it is verified that it has transformation application value and can be used as a new biomarker for early prediction of ICAS progression and evolution.

[0026] The application scheme first reports phosphatidylinositol PI 18:0 / 22:6 and phosphatidylglycerol PG 18:1 / 18:2 as biomarkers for predicting ICAS progression and evolution, which has certain reference significance for elucidating the mechanism between lipid metabolites and ICAS progression, especially, has important contribution and significance for the development of prediction products for ICAS occurrence and development, and has important clinical application prospect for early prediction and prevention of ICAS. BRIEF DESCRIPTION OF DRAWINGS

[0027] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0028] Figure 1 Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) is used to discover the ICAS queue.

[0029] Figure 2 To verify the orthogonal partial least squares discriminant analysis (OPLS-DA) for queue ICAS;

[0030] Figure 3 To identify the fold change in the subclass level of ICAS and the subclass without ICAS control in the cohort;

[0031] Figure 4 To discover volcano plots of differentially metabolized metabolites in the cohort;

[0032] Figure 5 To validate the fold change analysis of the subclass level of ICAS and the subclass without ICAS control in the cohort;

[0033] Figure 6 To validate the volcano plot of differentially metabolites in the cohort;

[0034] Figure 7 A Venn diagram illustrating the overlap of differentially metabolites between the discovery and validation cohorts;

[0035] Figure 8 To identify significantly differentially expressed metabolite levels in both the cohort and validation cohort;

[0036] Figure 9 Logistic regression association analysis between differentially metabolites and ICAS after multivariate correction;

[0037] Figure 10 A single-sample Mendelian randomized causal association inference plot between metabolites and ICAS;

[0038] Figure 11 To compare baseline concentrations of PG 18:1 / 18:2 and PI 18:0 / 22:6 in patients with stable ICAS and those with progressive ICAS during follow-up.

[0039] Figure 12 Forest plot of odds ratio (OR) and 95% confidence interval (CI) to assess the association between baseline metabolite levels and the risk of ICAS progression in a multivariate logistic regression model;

[0040] Figure 13 ROC curves are used to demonstrate the predictive performance of the three models on the progression of ICAS in the total population.

[0041] Figure 14 To show the ROC curve of the prediction performance of the three models on the progression of ICAS in the hypertensive population. DETAILED DESCRIPTION

[0042] The present application will be described in detail below with reference to examples, but the present application is not limited to these examples. Unless otherwise specified, the raw materials and reagents in the examples of the present application are purchased through commercial channels.

[0043] In the present application, RICAS cohort (Rose asymptomatic IntraCranial Artery Stenosi Explore, Shandong Pingyin County Kongcun Town Asymptomatic Intracranial Artery Atherosclerosis Cohort) data are used in combination with external database data for single-sample MR analysis, causal correlation between lipid metabolites and ICAS is analyzed, and serum biomarkers phosphatidylinositol PI 18:0 / 22:6 and phosphatidylglycerol PG 18:1 / 18:2 for the occurrence and development of ICAS are screened and obtained by combining ICAS characteristic metabolic changes with causal correlation analysis. ICAS progression is collected through prospective follow-up of the RICAS cohort, and the correlation between phosphatidylinositol PI 18:0 / 22:6 and phosphatidylglycerol PG 18:1 / 18:2 and ICAS progression is established, verifying that it has transformation application value and can be used as a new biomarker for early prediction of ICAS progression and evolution.

[0044] The present application will be described in detail below with reference to examples, but the present application is not limited to these examples. Unless otherwise specified, the raw materials and reagents in the examples of the present application are purchased through commercial channels.

[0045] Example 1

[0046] The experimental methods of the present study include the following contents:

[0047] 1) Study subjects and sample collection

[0048] The RICAS cohort is a prospective population cohort study. During the period from September 2017 to October 2018, 2474 rural participants (age ≥ 40 years, no previous stroke or transient ischemic attack) were recruited from four villages in Kongcun County, China. During the period from September 2021 to November 2022, nearly 1200 participants were additionally recruited in two adjacent villages in Kongcun County according to the same inclusion criteria.

[0049] In this study, 345 subjects enrolled in 2017 (including 110 ICAS cases and 235 non-ICAS controls) were extracted from the RICAS cohort as the discovery cohort, and 145 participants enrolled in 2021 (including 67 ICAS cases and 78 non-ICAS controls) as the validation cohort. All participants in the discovery cohort and the validation cohort underwent serum non-targeted lipidomics and metabolomics analysis, ICAS detection and clinical baseline characteristics collection (including age, gender, body mass index, smoking and drinking history, hypertension, diabetes, dyslipidemia, clinical lipid profile, high-sensitivity C-reactive protein (hsCRP). In addition, participants in the discovery set were subjected to whole genome sequencing. From September 2021 to November 2022, the discovery cohort was followed up prospectively, and ICAS progression was collected through face-to-face interviews, image evaluation and medical record review. All subjects signed informed consent.

[0050] 2) Non-targeted metabolomics detection

[0051] To 50 μL of plasma sample, 400 μL of pre-cooled internal standard methanol solution was added, vortexed and centrifuged to remove protein. The resulting extract was freeze-dried, and a water / methanol mixture (4:1, v / v) was used for reconstitution. After the sample was fully dissolved and centrifuged, the supernatant was analyzed by liquid chromatography-mass spectrometry (LC-MS). The internal standards used for metabolomics analysis and their final concentrations are as follows: carnitine C2:0-d3 (0.03 μg / mL), carnitine C10:0-d3 (0.02 μg / mL), carnitine C16:0-d3 (0.025 μg / mL), lysophosphatidylcholine 19:0 (LPC 19:0, 0.125 μg / mL), free fatty acid C16:0-d3 (FFA C16:0-d3, 0.4 μg / mL), free fatty acid C18:0-d3 (FFA C18:0-d3, 0.4 μg / mL), cholic acid-d4 (CA-d4, 0.3 μg / mL), chenodeoxycholic acid-d4 (CDCA-d4, 0.3 μg / mL), phenylalanine-d5 (Phe-d5, 0.5 μg / mL), leucine-d3 (Leu-d3, 0.7 μg / mL), and tryptophan-d5 (Trp-d5, 0.6 μg / mL).

[0052] Metabolite analysis was performed on an ultra-performance liquid chromatography system (Shimadzu) coupled with a Triple TOF 5600+ mass spectrometer (AB SCIEX, Framingham, USA). Waters BEH C8 column (2.1 mm x 50 mm, 1.7 μm) and HSS T3 column (2.1 mm x 50 mm, 1.8 μm) were used for separation in positive and negative ion modes, respectively. The flow rate was set at 0.4 mL / min, and the column temperature was maintained at 60 °C. The elution gradient program was as follows: initial B phase ratio was 5% for 0.5 min; linearly increased to 40% B within 2.0 min; increased to 100% B at 8.0 min and maintained for 2 min; then returned to 5% B within 0.1 min and equilibrated for 1.9 min. The mobile phase was water (containing 0.1% formic acid, A phase) and acetonitrile (containing 0.1 mM formic acid, B phase) in positive ion mode, and water (containing 6.5 mM ammonium bicarbonate, A phase) and 95% methanol (containing 6.5 mM ammonium bicarbonate, B phase) in negative ion mode.

[0053] The mass spectrometry parameters were set as follows: the sheath gas and curtain gas flow rates were 55 psi and 35 psi, respectively; the first scan range was set to m / z 100-1250, and the collision energy was 10 V; the data-dependent mass spectrometry scan (dd-MS2) range was set to m / z 50-1250, and the collision energy was 35 ± 15 V. The ion spray voltage was 5.5 kV, and the capillary temperature was 550 °C in positive ion mode; the ion spray voltage was -4.5 kV, and the capillary temperature was 450 °C in negative ion mode.

[0054] 3) Non-targeted lipidomics analysis

[0055] Lipid extraction was performed using the methanol / water / methyl tert-butyl ether (MeOH / H2O / MTBE) method. Briefly, 40 μΐ^of plasma sample was added with 300 μΐ^of methanol solution containing internal standards, vortexed to mix; then 1 mL of methyl tert-butyl ether (MTBE) was added, vortexed for 10 minutes; 300 μΐ^of ultrapure water was added, vortexed to form a two-phase system; after centrifugation, 400 μΐ^of supernatant was taken for freeze-drying, and stored at -80 °C. Before analysis, the dried sample was reconstituted with acetonitrile / isopropanol / water solution (65:30:5, v / v / v) containing 5 mM ammonium acetate, and 5 μΐ^was injected for analysis. The internal standards used for lipidomics analysis and their final concentrations were as follows: phosphatidylcholine 38:0 (PC 38:0, 1.67 μg / mL), phosphatidylethanolamine 34:0 (PE 34:0, 0.83 μg / mL), lysophosphatidylcholine 19:0 (LPC 19:0, 0.67 μg / mL), sphingomyelin 12:0 (SM 12:0, 0.83 μg / mL), triglyceride 45:0 (TG 45:0, 1.33 μg / mL), ceramide 17:0 (Cer 17:0, 0.33 μg / mL), free fatty acid 16:0-d3 (FFA 16:0-d3, 0.67 μg / mL), and free fatty acid 18:0-d3 (FFA 18:0-d3, 0.67 μg / mL).

[0056] The lipidomics analysis was performed using a Waters ACQUITY UHPLC system (Shimadzu Corporation) coupled with an AB SCIEX TripleQ-TOF 5600 Plus mass spectrometer (Concord, Canada). The Waters BEH C8 column (2.1 mm × 100 mm, 1.7 μιη) was used for lipid separation. The mobile phase was acetonitrile / water (3:2, v / v, containing 10 mM ammonium acetate, phase A) and isopropanol / acetonitrile (9:1, v / v, containing 10 mM ammonium acetate, phase B). The flow rate was set at 0.26 mL / min, and the column temperature was maintained at 55 °C. The elution gradient program was as follows: the initial B phase ratio was 32%, maintained for 1.5 minutes; linearly increased to 85% B within 15.5 minutes; increased to 97% B at 15.6 minutes, and maintained for 2.4 minutes; then returned to 32% B within 0.1 minute, and equilibrated for 1.9 minutes.

[0057] The mass spectrometry parameters were set as follows: the ion spray voltages for positive and negative ion modes were 5500 V and 4500 V, respectively; the interface heating temperatures for positive and negative modes were 500°C and 550°C, respectively. In positive ion mode, the ion source gas 1, ion source gas 2, and curtain gas flow rates were 50, 50, and 35 psi, respectively; in negative ion mode, they were 55, 55, and 35 psi, respectively. The scan ranges for positive and negative ion modes were 300–1250 Da and 150–1250 Da, respectively.

[0058] 4) Genomic testing and Mendelian randomization analysis

[0059] DNA samples extracted from peripheral blood were genotyped on a screening array targeting Asian populations, covering all participants in the discovery group. All samples underwent quality control, excluding those with a call-out rate below 95%, abnormal heterozygosity (±6 standard deviations), kinship (PI-HAT>0.3), or population outliers identified by principal component analysis. Other exclusions included highly chromosomal variations and those not conforming to Hardy-Weinberg equilibrium (p<1×10⁻⁶). -4 After samples with allele frequencies below 0.01 or call-out rates above 5%, genotyping was performed using IMPUTE2 (version 2.3.2) on the Phase III reference data of 1000 genome projects. This process provided 474,961 high-quality autosomal single nucleotide polymorphisms (SNPs) from 345 participants. After adjusting for age, sex, body mass index (BMI), and principal components of genetics, samples significantly associated with exposure factors (ICAS) (P < 1 × 10⁻⁶) were selected. -5 Single nucleotide variations. Clustering was performed within 10000 kb using linkage disequilibrium (LD) clustering. 2 The independence of the indicators was ensured by a value <0.001. Two-stage least squares regression in two-way single-sample MR was used to estimate the causal effects. The strength of the indicators was assessed by the F-statistic, which ranged from 5.19 to 588.51; two analyses were below the conventional threshold of 10 (metabolite chanoclavine: F = 5.19; ICAS: F = 6.63).

[0060] 5) ICAS Detection and Progress Assessment

[0061] At baseline, all participants underwent ICAS assessment via transcranial Doppler (TCD). Subjects initially screened for ICAS using TCD based on SONIA criteria were further validated via microscopic resection of the lesion using macroscopic resection of the lesion, and the degree of stenosis was determined using the WASID criteria. During follow-up, progression was assessed using the same ICAS diagnostic procedure as at baseline. TCD progression was defined as the appearance of a new lesion according to the same criteria as baseline assessment, or an increase in mean blood flow velocity ≥30 mm / s, accompanied by indicators supporting progression according to SONIA criteria. Progression was defined by MRA as a new ICAS lesion or an increase in the degree of stenosis according to the WASID criteria.

[0062] 6) Data Analysis

[0063] The results were combined after deduplication of non-targeted metabolomics and lipidomics.

[0064] Through integrated lipid and metabolomics analysis, OPLS-DA analysis revealed, without overfitting, a segregation of overall metabolite expression between ICAS subjects in the cohort and validation cohort.

[0065] like Figure 1 and Figure 2 As shown, this is an orthogonal partial least squares discriminant analysis (OPLS-DA) score plot of serum metabolomics data for patients with intracranial arteriosclerosis (ICAS) (blue) and controls without ICAS (red). Figure 1 Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) is used to discover the ICAS queue. Figure 2 To verify the orthogonal partial least squares discriminant analysis (OPLS-DA) for queue ICAS. Figure 1 The cohort (N=345; 110 ICAS cases, 235 non-ICAS controls) showed significant intergroup separation (model parameters: R0). 2 X=0.574, R 2 Y=0.829, Q 2 =0.625; R 2 Y and Q 2 (permutation p<.001). Figure 2 The predictive performance of the model was validated in an independent cohort (N=145), which also showed significant discriminative power (model parameters: R). 2 X=0.836, R 2 Y=0.694, Q 2 =0.151; R 2 Y and Q 2 The permutation p = 0.002. Each point represents a patient and is plotted based on the predicted (p1) and first orthogonal (o1) components. Ellipses represent the 95% confidence interval for each group.

[0066] As shown in FIGS. 1A and 1B, the metabolite subgroups and individual level changes in the discovery cohort and validation cohort were discovered. Figure 3 Figure 4 Figure 5 Figure 6 The fold change analysis of subgroups level between ICAS and non-ICAS controls in the discovery cohort showed significant increase in sphingolipids (Cer) and secondary metabolites, and significant decrease in phosphatidylinositol (PI), phosphatidylethanolamine (PE), cholesteryl ester (CE) and sphingomyelin (SM) (*FDR<0.05); Figure 3 The volcano plot of differential metabolites in the discovery cohort, significant difference threshold: |log2FC|>log21.2 and FDR<0.05; up- and down-regulated metabolites are represented by red and blue colors, respectively; Figure 4 The fold change analysis of subgroups level between ICAS and non-ICAS controls in the validation cohort showed significant decrease in cholesteryl ester (CE), phosphatidylglycerol (PG) and phosphatidylinositol (PI) (*FDR<0.05); Figure 5 The volcano plot of differential metabolites in the validation cohort, significant difference threshold: |log2FC|>log21.2 and FDR<0.05; up- and down-regulated metabolites are represented by red and blue colors, respectively. Figure 6

[0067] As shown in FIGS. 2A and 2B, the stable differential expression metabolite changes in the discovery cohort and validation cohort were discovered. Figure 7 Figure 8 Figure 9 The Venn diagram showed the overlap of differential metabolites between the discovery cohort and validation cohort; Figure 7 The metabolite levels of the common significant differential expression between the discovery cohort and validation cohort showed that the contents of these phospholipids in ICAS were consistently decreased in both datasets, including PG 18:1 / 18:2, PI 16:0 / 18:2, PI 16:0 / 20:4 and PI 18:0 / 22:6. The data showed the discovery and validation groups, respectively; significance level: ***P<0.001, ****P<0.0001; Figure 8 The logistic regression association analysis of differential metabolites (PG 18:1 / 18:2, PI 16:0 / 18:2, PI 16:0 / 20:4 and PI 18:0 / 22:6) with ICAS after multivariate correction. Model 1: adjusted for age and gender; Model 2: further adjusted for body mass index, smoking habit, drinking habit, hypertension, diabetes, dyslipidemia and high-sensitivity C-reactive protein. Figure 9

[0068] ​​​​​​​In summary, in the discovery cohort, ICAS participants showed significantly elevated levels of ceramide (Cer), fatty acids (FA), and secondary metabolites, while cholesterol esters (CE), phosphatidylinositol (PI), phosphatidylethanolamine (PE), and ceramide (SM) levels were significantly decreased. In the validation cohort, significantly reduced CE and PI levels were also observed in ICAS participants. Analysis of individual lipid metabolites using |log2FC|>log21.2 and FDR<0.05 as significant differential expression thresholds revealed significantly reduced levels of PI18:0 / 22:6, PI16:0 / 20:4, PI16:0 / 18:2, and PG18:1 / 18:2 in both the discovery and validation sets of ICAS participants. After multivariate adjustment, PI18:0 / 22:6, PI16:0 / 20:4, PI16:0 / 18:2, and PG18:1 / 18:2 remained significantly associated with ICAS.

[0069] Single-sample Mendelian randomization (MR) analysis was performed on 631 lipid metabolites using genetic data from the discovery cohort.

[0070] like Figure 10 The image shows a single-sample Mendelian randomized causal association inference between metabolites and ICAS. The forest plot shows the positive (left, orange) and negative (right, blue) MRI results, where the odds ratio (OR) and 95% confidence interval (CI) are plotted logarithmically. Filled points indicate statistically significant associations after correction for multiple tests. CE represents cholesterol esters; Cer represents ceramides; DG represents diglycerides; FA represents fatty acids; HexCer represents hexosylsphingosine; LPC represents lysophosphatidylcholine; LPE represents lysophosphatidylethanolamine; PC represents phosphatidylcholine; PE represents phosphatidylethanolamine; PG represents phosphatidylglycerol; PI represents phosphatidylinositol; PS represents phosphatidylserine; SM represents sphingomyelin; TG represents triglycerides.

[0071] In summary, ICAS-related risk factors are mainly concentrated in metabolites of fatty acids (FA), ceramides (Cer), phosphatidylcholine (PC), lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE), and phosphorylphosphatidylcholine (PC) subclasses; protective factors are mainly concentrated in phosphatidylinositol (PI), phosphatidylglycerol (PG), cholesterol esters (CE), phosphatidylserine (PS), phosphatidylethanolamine (PE), and ceramides (SM). Among the four metabolites with significantly reduced expression levels in ICAS subjects identified in the above lipid metabolomics analysis, PI 18:0 / 22:6 and PG 18:1 / 18:2 also showed a potential protective causal association with ICAS in single-sample MR analysis.

[0072] Example 2

[0073] Prospective follow-up in the discovery cohort was continued to validate the predictive role of PI 18:0 / 22:6 and PG 18:1 / 18:2 for ICAS progression.

[0074] As shown in Figure 11 , Figure 12 , Figure 13 and Figure 14 , the causally associated differential metabolites had predictive roles for ICAS progression. Figure 11 Comparing the baseline concentrations of PG 18:1 / 18:2 and PI 18:0 / 22:6 between patients with stable ICAS and those with progressive ICAS during follow-up, the results showed that the concentrations were significantly lower in the progressive group; Figure 12 Forest plot of odds ratios (OR) and 95% confidence intervals (CI) for the association between baseline metabolite levels and the risk of ICAS progression in multivariable logistic regression models: Model 1 (adjusted for age and sex), Model 2 (further adjusted for BMI, smoking habit, alcohol consumption habit, hypertension, diabetes, dyslipidemia, and hs-CRP), and Model 3 (further adjusted for follow-up time). PI 18:0 / 22:6 showed an independent protective association with ICAS progression, while the association of PG 18:1 / 18:2 only showed a borderline significant association.

[0075] Figure 13 The ROC curves showed the predictive performance for ICAS progression in the total population based on the three models: Model 1, based on baseline clinical variables only, including age, sex, diabetes, and dyslipidemia (orange line; AUC = 0.499, 95% confidence interval: 0.40-0.57); Model 2, based on the causally associated differential metabolites only (green line; AUC = 0.57, 95% confidence interval: 0.50-0.65); and Model 3, combining baseline clinical variables and causally associated differential metabolites (blue line; AUC = 0.60, 95% confidence interval: 0.53-0.68). The diagonal dashed line represents the line with no discriminative power (AUC = 0.5).

[0076] Figure 14The ROC curves demonstrate the predictive performance of the three models for the progression of ICAS in the hypertensive population: Model 1, based on baseline clinical variables only, including age, sex, diabetes, and dyslipidemia (orange line; AUC = 0.54, 95% confidence interval: 0.45-0.63); Model 2, based on the causally associated differential metabolites only (green line; AUC = 0.60, 95% confidence interval: 0.52-0.68); and Model 3, combining baseline clinical variables and causally associated differential metabolites (blue line; AUC = 0.63, 95% confidence interval: 0.55-0.71). The diagonal dashed line represents the line of no discrimination (AUC = 0.5).

[0077] In summary, the progression of ICAS was significantly associated with lower baseline PI 18:0 / 22:6 and PG 18:1 / 18:2 levels. After adjusting for age, sex, smoking, alcohol consumption, hypertension, diabetes, dyslipidemia, follow-up time, and other factors, the baseline PI 18:0 / 22:6 level was still independently associated with the progression of ICAS, and the baseline PG 18:1 / 18:2 level was critically significantly associated with the progression of ICAS. In the total population, the ROC prediction of PI 18:0 / 22:6 and PG 18:1 / 18:2 combined with clinical characteristics (age, sex, diabetes, and dyslipidemia) was superior to that of clinical characteristics alone. In particular, in the hypertensive population, the ROC prediction effect of PI 18:0 / 22:6 and PG 18:1 / 18:2 combined with clinical characteristics (age, sex, diabetes, and dyslipidemia) was significantly superior to that of clinical characteristics alone.

[0078] This further confirms that PI 18:0 / 22:6 and PG 18:1 / 18:2 can indeed play a definite role as novel serum biomarkers in predicting the progression of ICAS.

[0079] Example 3: Prediction model

[0080] In the discovery cohort, all the subjects with hypertension who completed follow-up were used as the training sample, and the bootstrap method was used to train 1000 times to obtain the prediction model. For the hypertensive population, PI 18:0 / 22:6 combined with PG 18:1 / 18:2 and the clinical baseline characteristics of age, gender, diabetes, and dyslipidemia can better predict the future ICAS stenosis progression: P prediction =-1.7812-0.5035*scaled(PI 18:0 / 22:6) (z-scale transformation after internal standard correction)-0.3814*scaled(PG 18:1 / 18:2) (z-scale transformation after internal standard correction)+0.3654*whether dyslipidemia (no=0, yes=1)+0.0099*age-0.5788*gender (male=1, female=2)+0.5805*whether diabetes (no=0, yes=1). The model AUC=0.632, sensitivity: 0.65, specificity: 0.574, (compared with the simple clinical characteristic prediction model AUC=0.549, sensitivity=0.35, specificity=0.832, the overall prediction performance of the model has been significantly improved), the cutoff value of the model is 0.268, when P prediction is greater than 0.268, it is considered that the subsequent ICAS progression is more likely to occur, and when P prediction is less than 0.268, it is considered that the possibility of subsequent ICAS progression is low.

[0081] For the whole population, the model for predicting the future ICAS stenosis progression of PI 18:0 / 22:6 combined with PG 18:1 / 18:2 and the clinical baseline characteristics of age, gender, diabetes, and dyslipidemia ((compared with the simple clinical characteristic prediction model AUC=0.488, sensitivity=0.162, specificity=1, compared with the clinical model, although the improvement is not significant): P prediction =-1.6681-0.3501*scaled(PI 18:0 / 22:6) (z-scale transformation after internal standard correction)-0.1219*scaled(PG 18:1 / 18:2) (z-scale transformation after internal standard correction)+0.0531*whether dyslipidemia (no=0, yes=1)+0.0097*age-0.604*gender (male=1, female=2)+0.7644*whether diabetes (no=0, yes=1). The model AUC=0.603, sensitivity: 0.486, specificity: 0.697, cutoff value 0.277, when P prediction is greater than 0.277, it is considered that the subsequent ICAS progression is more likely to occur, and when P prediction is less than 0.277, it is considered that the possibility of subsequent ICAS progression is low.

[0082] The above merely illustrates the embodiments of the present application, and the protection scope of the present application is not limited to these specific embodiments, but determined by the claims of the present application. The present application can have various changes and modifications for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the technical thought and principle of the present application shall be included in the protection scope of the present application.

Claims

1. The application of biomarkers phosphatidylinositol and phosphatidylglycerol in the preparation of products predicting the progression of intracranial atherosclerosis, characterized in that, The phosphatidylinositol is PI 18:0 / 22:6, and the phosphatidylglycerol is PG 18:1 / 18:

2.

2. The application according to claim 1, characterized in that, The samples used in the progress prediction process are derived from one or more of serum, plasma, and blood.

3. The application of reagents for detecting the levels of biomarkers phosphatidylinositol and phosphatidylglycerol in the preparation of products predicting the progression of intracranial atherosclerosis, characterized in that... The phosphatidylinositol is PI 18:0 / 22:6, and the phosphatidylglycerol is PG 18:1 / 18:

2.

4. The application according to claim 3, characterized in that, The samples used in the progress prediction process are derived from one or more of serum, plasma, and blood.

5. A reagent for predicting the progression of intracranial atherosclerosis, characterized in that, The progress prediction reagent includes components for specifically detecting the levels of the biomarkers phosphatidylinositol and phosphatidylglycerol, wherein the phosphatidylinositol is PI 18:0 / 22:6 and the phosphatidylglycerol is PG 18:1 / 18:

2.

6. A kit for predicting the progression of intracranial atherosclerosis, characterized in that, The progress prediction kit includes the progress prediction reagent as described in claim 5.

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