WO2023010752A1

WO2023010752A1 - Marker, detection method, and kit for evaluating cardiac injury of schizophrenia patient

Info

Publication number: WO2023010752A1
Application number: PCT/CN2021/138095
Authority: WO
Inventors: 张志珺; 栾迪
Original assignee: 中国科学院深圳先进技术研究院
Priority date: 2021-08-02
Filing date: 2021-12-14
Publication date: 2023-02-09
Also published as: CN115701540A

Abstract

A marker, a detection method, and a kit for evaluating the cardiac injury of a schizophrenia patient. A detection reagent for evaluating the cardiac injury of a schizophrenia patient comprises a reagent for extracting indole-3-lactic acid from serum and a reagent for quantitatively detecting indole-3-lactic acid. According to the serum detection method for evaluating the cardiac injury of a schizophrenia patient, a metabolite associated with the cardiac injury of the schizophrenia patient is used as a marker, such that the cardiac injury condition can be obtained more quickly, and the compliance of the patient is improved.

Description

A marker, detection method and kit for assessing cardiac damage in patients with schizophrenia

technical field

The invention belongs to the field of medical detection, and in particular relates to a marker, a detection method and a kit for evaluating heart damage in patients with schizophrenia.

Background technique

An important cause of death in patients with schizophrenia is heart damage, and early recognition is particularly important. In the detection of heart injury, the 24-hour ambulatory electrocardiogram is more able to reflect the patient's heart injury and the state of the heart in dynamic and resting conditions. It is an important detection method for heart injury. It is difficult to obtain accurate data of 24-hour ambulatory electrocardiogram, and then it is impossible to obtain various indicators of the heart.

How to quickly obtain heart-related data of schizophrenic patients and reduce the influence of mental state of schizophrenic patients on the test results is an urgent problem to be solved.

Contents of the invention

Based on the above-mentioned problems in the prior art, the present invention provides a biomarker that can be used to assess heart damage in schizophrenia and its detection methods and kits.

One aspect of the present invention provides a detection reagent for evaluating cardiac damage in patients with schizophrenia, wherein the detection reagent includes a reagent for detecting indole-3-lactic acid in serum.

Further, the reagents include reagents for extracting indole-3-lactic acid from serum and reagents for quantitatively detecting indole-3-lactic acid.

Another aspect of the present invention provides a kit for assessing cardiac damage in patients with schizophrenia, which includes the above-mentioned detection reagent.

Another aspect of the present invention provides the use of the above-mentioned detection reagent in the preparation of a kit or detection preparation for assessing cardiac injury in patients with schizophrenia.

Further, the heart injury condition is selected from abnormalities in the 24-hour ambulatory electrocardiogram, and further, the abnormalities are selected from hidden arrhythmias that are easily missed by conventional electrocardiograms; preferably tachyarrhythmia, bradyarrhythmia, Ectopic rhythm, conduction block, arrhythmia leading to sudden cardiac death, myocardial infarction, silent myocardial ischemia.

Another aspect of the present invention provides a method for assessing heart damage in patients with schizophrenia, the method comprising the following steps:

1) obtaining a serum sample of a subject suffering from schizophrenia, extracting indole-3-lactic acid from the serum sample by means of an extraction reagent, and detecting the concentration of indole-3-lactic acid in the serum;

2) Judging whether the subject suffers from heart damage by the concentration of indole-3-lactic acid in the serum.

Further, the method for detecting the concentration of indole-3-lactic acid in serum is selected from HPLC method or HPLC combined with mass spectrometry method.

Further, if the concentration of indole-3-lactic acid in the serum is higher than the normal value, it is considered to have heart damage.

Another aspect of the present invention provides a marker for assessing cardiac damage in patients with schizophrenia, the marker being selected from indole-3-lactic acid.

Another aspect of the present invention provides a method for screening serum metabolites as a marker for assessing cardiac injury in patients with schizophrenia, the method comprising the following steps:

i) Take schizophrenia patients as the first screening cohort, which includes schizophrenia patients with heart damage and schizophrenia patients without heart damage; through metabolomics research, the screening cohort with heart damage As well as the metabolites in the serum of the screening cohort without heart damage were detected and data analyzed, and the differential metabolites between groups were screened;

ii) Take patients with schizophrenia as the second screening cohort, conduct 24-hour dynamic electrocardiogram detection on the second screening cohort, and divide them into the non-abnormal group and the abnormal group according to the 24-hour dynamic electrocardiogram results; detect the non-abnormal group and the abnormal group respectively The content or concentration of the differential metabolites between the groups in the serum of each group is statistically analyzed, and the screening of the differential metabolites with statistical differences between the groups is the marker for evaluating the cardiac damage in patients with schizophrenia.

Further, in step i), the ratio of the number of schizophrenia patients with heart damage to the schizophrenia patients without heart damage is 1:0.5-1.5.

Further, in step ii), the ratio of the number of people in the non-abnormal group to the abnormal group is 1:0.5-1.5.

Further, in step i), the method for detecting metabolites is a method of high performance liquid chromatography coupled with mass spectrometry (LC-MS).

Further, in step i), the method of data analysis is PCA analysis with SIMCA-P software, and on this basis, the model construction of PLS-DA and OPLS-DA is carried out, so as to obtain the differential metabolites between groups.

Beneficial effect

1) The present invention breaks the technical prejudice and provides a serum detection method for assessing heart damage in patients with schizophrenia. None of the prior art mentions that schizophrenia can be assessed by the content of metabolites in serum through the method of metabolomics Patient with cardiac injury. It is well known that heart injury is an important factor affecting the life and health of patients with schizophrenia, and the unstable mental state of schizophrenic patients makes it difficult to diagnose and treat heart injury. Especially the ambulatory electrocardiogram that needs to detect for a long time, compliance is very low. The present invention creatively screens the metabolites in the serum, and finds the metabolites related to the heart damage of patients with schizophrenia as markers in the metabolites, which can obtain the heart damage status more quickly and improve the patient's compliance.

2) The biomarker of the present invention can be detected by plasma, the detection method is simple, and the detection efficiency is higher than that of the 24-hour dynamic electrocardiogram.

Description of drawings

Fig. 1 is total PCA score map (ESI+) in embodiment 1.

Figure 2 is the total PCA score graph (ESI-) in Example 1.

Fig. 3 is the PCA score figure (ESI+) of A-B two groups in embodiment 1.

Fig. 4 is the PCA score chart (ESI-) of A-B two groups in embodiment 1.

Fig. 5 is the PLS-DA score map and the sequence verification map (ESI+) of the A-B two groups in Example 1.

Fig. 6 is the PLS-DA score diagram and the sequence verification diagram (ESI-) of the A-B groups in Example 1.

Fig. 7 is the OPLS-DA score graph (ESI+) of A-B group in embodiment 1.

Fig. 8 is the OPLS-DA score map (ESI-) of the A-B group in Example 1.

Fig. 9 is a statistical graph (mean ± standard deviation) in Example 2.

Detailed ways

In order to make the above objects, features and advantages of the present invention more obvious and understandable, the specific implementation modes of the present invention will be described in detail below, but they should not be construed as limiting the scope of implementation of the present invention.

Example 1 Screening Metabolites in Small Samples

For the screening cohort, 40 patients with schizophrenia were selected, including 20 patients with heart injury group A and 20 patients without heart injury group B. Peripheral blood was collected from 40 patients, and LC-MS metabolomics research was carried out, and the differential metabolites found between groups were screened, identified, pathway assigned and clustered.

Sample pretreatment

1.1. The patient's serum sample was thawed at room temperature, and 100 μL of the sample was pipetted into a 1.5 mL EP tube with a pipette gun.

1.2. Add 300 μL of methanol, vortex and mix for 30 seconds.

1.3. Stand at -40°C for 1h.

1.4. Vortex and mix for 30s, and let stand at 4°C for 0.5h.

1.5. Place in a centrifuge at 4°C and centrifuge at 12000rpm for 15min.

1.6. Take out all the supernatant in a centrifuge tube and let stand at -40°C for 1h.

1.7. Place in a centrifuge at 4°C and centrifuge at 12000rpm for 15min.

1.8. Aspirate 200 μL of supernatant, add 5 μL of internal standard (140 μg/mL, 2-chlorophenylalanine), and transfer to the injection vial for detection.

LC/MS analysis

2.1. Instrument analysis platform: LC-MS (Waters, UPLC; Thermo, Q Exactive)

2.2. Chromatographic column: Waters ACQUITY UPLC HSS T3 column (2.1mm×100mm, 1.8μm)

2.3. The chromatographic separation conditions are: column temperature is 40°C; flow rate is 0.3mL/min;

Mobile phase composition A: water + 0.05% formic acid, B: acetonitrile;

The injection volume was 6 μL, and the autosampler temperature was 4°C.

The mobile phase gradient elution program is shown in Table 1.

Table 1. Mobile Phase Elution Program

Mass spectrometry detection parameters:

Positive mode: heater temperature 300°C; sheath gas flow: 45arb; auxiliary gas flow: 15arb; exhaust gas flow: 1arb; electrospray voltage: 3.0KV; capillary temperature: 350°C; S-Lens RF Level, 30%.

Negative mode: heater temperature 300°C; sheath gas flow: 45arb; auxiliary gas flow: 15arb; exhaust gas flow: 1arb; electrospray voltage: 3.2KV; capillary temperature: 350°C; S-Lens RF Level, 60%.

data analysis

Use Compound Discoverer software (Thermo Company) to extract and preprocess the LC/MS detection data, and normalize and post-edit the data in Excel 2010, and finally arrange it into a two-dimensional data matrix form, including retention time (RT( Retention time)), molecular weight (CompMW), observed amount (sample name), number of extractable substances (ID) and peak intensity. In this project, a total of 2894 Features were obtained in the positive mode, and 2186 Features were obtained in the negative mode. The edited data matrix was imported into SIMCA-P 13.0 (Umetrics AB, Umea, Sweden) software for multivariate statistical scores.

Multivariate Statistical Analysis

- Overall PCA analysis

The principal component analysis of the samples can generally reflect the metabolic difference between the two groups of samples and the variability of the samples within the group. In order to obtain more intuitive and reliable results, the purpose of normalization is to make the scale of all variables (a certain numerical feature, such as mean and standard deviation) on the same level, so as to avoid the concentration difference of different metabolites in complex biological samples. The signal of some metabolites with high or low concentration caused by large concentration is masked, which affects the identification of biomarkers.

In order to determine whether there is a difference between the two groups, the PCA modeling method was used to analyze the samples. In this analysis, a total of 6 principal components were obtained in the positive mode, and the cumulative R ² X = 0.495, Q ² = 0.257; in the negative mode, a total of 10 principal components, cumulative R ² X =0.571, Q ² =0.0281. The PCA score plot (Scores plot) is shown in Figure 1 and Figure 2.

For unsupervised model analysis such as PCA, the main parameter to judge the quality of the model is R ² X, which represents the interpretation rate of the model. PCA analysis is a non-supervised model analysis method. The model can be based on Compared with supervised model analysis methods such as PLS-DA analysis, PCA can more truly reflect the difference between groups and identify the variation within the group.

-Group A-B

PCA analysis

Principal component analysis was carried out on group B and group A. In this analysis, 5 principal components were obtained in the positive mode, and the cumulative R ² X = 0.467, Q ² = 0.233; 8 principal components were obtained in the negative mode, and the cumulative R ² X =0.519, Q ² =0.0239. The PCA score plot (Scores plot) is shown in Figure 3 and Figure 4:

In order to obtain the metabolite information leading to this significant difference, a supervised multidimensional statistical method, Partial Least Squares Discriminant Analysis (PLS-DA), was further used for statistical analysis of the two groups of samples.

Its model quality parameters are: in positive mode, with 2 principal components, R ² X = 0.225, R ² Y = 0.804, Q ² = -0.163; in negative mode, with 2 principal components, R ² X = 0.13, R ² Y = 0.821, Q ² = -0.21. The main parameters to judge the quality of the model are R ² Y (the value represents the interpretation rate of the model) and Q ² value (the value is the prediction rate of the model). In addition, the model will be sorted and verified to check whether the model is "overfitting". The model is unreliable for explaining the difference between the two groups and finding different substances, so it is not appropriate to use this data for subsequent analysis.

Whether the model is "overfitting" reflects whether the model is constructed accurately. The lack of "overfitting" indicates that the model can describe the sample well and can be used as a prerequisite for the search for model biomarker groups. "Overfitting" indicates that This model is not suitable for describing samples, nor is it suitable for post-analysis of this data.

The supervised method OPLS-DA was further used for modeling and analysis. As a result, one principal component and one orthogonal component were obtained in positive mode, R ² X = 0.225, R ² Y = 0.804, Q ² = -0.0237; negative mode One principal component and one orthogonal component are obtained, R ² X = 0.13, R ² Y = 0.821, Q ² = -0.373, the parameter R ² Y of the model represents the interpretation rate of the model, and Q ² represents the prediction rate of the model . Its score chart is shown in Figure 7 and Figure 8:

- Mining and identification of differential metabolites in groups A-B

The VIP (Variable Importance in the Projection) value of the OPLS-DA model (threshold > 1) was used, combined with the p value of the t-test (p < 0.05) to find differentially expressed metabolites. The qualitative method of the differential metabolites is: search the online database (Metlin) (comparing the mass-to-charge ratio m/z or accurate molecular mass mass of the mass spectrum).

Human serum samples were detected by LC/MS instrument. For model discriminant analysis on the response intensity data of the peaks of these substances in all samples, we first use SIMCA-P software for PCA analysis, and on this basis, model construction of PLS-DA and OPLS-DA to obtain differential expression Metabolites. The types of different substances mainly include amino acids, fatty acids, organic acids, etc. The main pathways affected are amino acid metabolism, fatty acid metabolism, lipid metabolism, etc.

The experimental results show that there are 14 molecules with differences:

1) Deoxycholic acid, 2) 20-Hydroxyeicosatetraenoic acid, 3) Lactate, 4) Cortisol, 5) Bilirubin Bilirubin, 6) methionine, 7) creatine, 8) choline, 9) propionylcarnitine, 10) cis-eicosan-11 - cis-gondoic acid (cis-gondoic acid), 11) indole acetic acid (indoleacetate, IAA), 12) indole-3-lactic acid (Indole-3-lactic acid), 13) dihydrothymine (Dihydrothymined), 14) Gamma-Glutamylleucine.

Through this experiment, it was found that 20 schizophrenia patients with the most severe heart damage were compared with the corresponding schizophrenia patients without heart damage. The index of 14 in the serum can be used as a potential criterion for judging the heart damage of schizophrenia patients .

Embodiment 2 large sample verification

These 14 metabolites were verified in 199 samples and compared with the 24-hour dynamic electrocardiogram data to further screen for markers that can be used as a standard for cardiac damage in patients with schizophrenia.

Metabolite extraction

Take 200 μL of plasma samples, add 800 μL of pre-cooled methanol/acetonitrile (1:1, v/v) to each sample, vortex mix, incubate at -20 °C for 1 h to precipitate protein, centrifuge at 3000 rcf at 4 °C for 20 min, and take the supernatant to vacuum dry. Add 300 μL of CH3OH/ACN/water (1:1:1, v/v/v) complex solution, centrifuge at 3000 rcf at 4°C for 20 min, and take the supernatant for analysis.

Chromatography-mass spectrometry

A high performance liquid chromatography conditions

The samples were separated by Agilent 1290 Infinity LC ultra-high performance liquid chromatography system. Hydrophilic chromatographic analysis mobile phase: liquid A is 50 mM ammonium acetate aqueous solution (containing 1.2% NH4OH), and liquid B is acetonitrile. The sample was placed in an autosampler at 4°C, the column temperature was 35°C, the flow rate was 300 μL/min, and the injection volume was 2 μL. The relevant liquid phase gradient is as follows: 0-3min, B solution linearly changes from 85% to 80%; 3-4min, B solution maintains at 80%; 4-6min, B solution linearly changes from 80% to 70%; 6-10min , B solution linearly changed from 70% to 50%; 10-12.5min, B solution was maintained at 50%; 12.5-12.6min B solution was linearly changed from 50% to 85%; 12.6-18min, B solution was maintained at 85%.

Mobile phase for reversed-phase chromatographic analysis: Solution A is 50 mM ammonium formate aqueous solution (containing 0.425% FA), and solution B is pure methanol. The sample was placed in an autosampler at 4°C, the column temperature was 40°C, the flow rate was 400 μL/min, and the injection volume was 2 μL. The relevant liquid phase gradient is as follows: 0-5min, B liquid changes linearly from 5% to 60%; 5-11min, B liquid changes linearly from 60% to 100%; 11-13min, B liquid maintains at 100%; min, B solution linearly changed from 100% to 5%; 13.1-16min, B solution maintained at 5%.

A QC sample is set at every interval of a certain number of experimental samples in the sample queue to detect and evaluate the stability and repeatability of the system.

B mass spectrometry

Mass spectrometry was performed using a 5500QTRAP mass spectrometer (AB SCIEX) in positive/negative ion mode. 5500QTRAP ESI source conditions are as follows:

The ion source parameters in positive ion mode are as follows:

Source temperature,450℃;

Gas

1,60;

Gas

2,60; CRU,30; ISVF,5000V

Negative ion mode ion source parameters are as follows:

Source temperature,450℃;

Gas

1,60;

Gas

2,60; CRU,30; ISVF,-5000V;

The MRM mode was used to detect the ion pairs to be tested, and the ion pair information of all neurotransmitters is shown in Appendix 1.

data processing

Use MultiQuant or Analyst software for quantitative analysis; determine the integral effect; perform statistical analysis according to the corresponding 24-hour dynamic electrocardiogram results, and the results are shown in the table below.

*The metabolite value of each sample is unitless and represents the corrected peak area, which belongs to relative quantification.

^#Abnormal refers to tachyarrhythmia, bradyarrhythmia, ectopic rhythm, conduction block, arrhythmia leading to sudden cardiac death, myocardial infarction, asymptomatic myocardial ischemia.

The patients were divided into two groups according to whether the ambulatory electrocardiogram was abnormal, and the one with statistical difference was indole-3-lactic acid, and the patients with schizophrenia with abnormal ambulatory electrocardiogram were higher than those without abnormality in ambulatory electrocardiogram. The statistical results are shown in Figure 9. It can be seen from the results of large sample data that the present invention has discovered a serological index that can be used as a serological indicator of heart damage in patients with schizophrenia, that is, indole-3-lactic acid, and can predict schizophrenia by detecting indole-3-lactic acid in serum It is very beneficial for the prediction of cardiac injury in patients with schizophrenia, because although it is very convenient and accurate to use 24-hour dynamic electrocardiogram for patients with non-psychiatric diseases, it is very convenient and accurate for patients with schizophrenia. Monitoring is very difficult, especially during the onset of the disease, patients are likely to not cooperate with monitoring, or tear off the monitoring device from the fixed part or destroy the device during the monitoring process. Relatively, the time for blood drawing is short, and it can usually be completed with the cooperation of patients or other auxiliary personnel, and the time for testing is shorter than the 24-hour electrocardiogram cycle. It can improve diagnostic efficiency, improve accuracy and patient compliance.

Claims

A detection reagent for assessing heart damage in patients with schizophrenia, characterized in that the detection reagent includes a reagent for detecting indole-3-lactic acid in serum;

Preferably, the heart injury condition is selected from abnormalities in the 24-hour ambulatory electrocardiogram, more preferably, the abnormality is selected from hidden arrhythmias that are easily missed by conventional electrocardiograms; most preferably tachyarrhythmia, bradyarrhythmia , ectopic rhythm, conduction block, arrhythmia leading to sudden cardiac death, myocardial infarction, asymptomatic myocardial ischemia.
The detection reagent according to claim 1, wherein the detection reagent comprises a reagent for extracting indole-3-lactic acid from serum and a reagent for quantitatively detecting indole-3-lactic acid.
A kit for assessing heart damage in patients with schizophrenia, characterized in that the kit includes the above detection reagents;

Preferably, the heart injury condition is selected from abnormalities in the 24-hour ambulatory electrocardiogram, more preferably, the abnormality is selected from hidden arrhythmias that are easily missed by conventional electrocardiograms; most preferably tachyarrhythmia, bradyarrhythmia , ectopic rhythm, conduction block, arrhythmia leading to sudden cardiac death, myocardial infarction, asymptomatic myocardial ischemia.
Use of the detection reagent of claim 1 in the preparation of a kit or detection preparation for assessing cardiac injury in patients with schizophrenia;

Preferably, the heart injury condition is selected from abnormalities in the 24-hour ambulatory electrocardiogram, more preferably, the abnormality is selected from hidden arrhythmias that are easily missed by conventional electrocardiograms; most preferably tachyarrhythmia, bradyarrhythmia , ectopic rhythm, conduction block, arrhythmia leading to sudden cardiac death, myocardial infarction, asymptomatic myocardial ischemia.
A marker for assessing cardiac damage in patients with schizophrenia, characterized in that the marker is selected from indole-3-lactic acid;

Preferably, the heart injury condition is selected from abnormalities in the 24-hour ambulatory electrocardiogram, more preferably, the abnormality is selected from hidden arrhythmias that are easily missed by conventional electrocardiograms; most preferably tachyarrhythmia, bradyarrhythmia , ectopic rhythm, conduction block, arrhythmia leading to sudden cardiac death, myocardial infarction, asymptomatic myocardial ischemia.
A method for screening serum metabolites as a marker for assessing heart damage in patients with schizophrenia, characterized in that the method comprises the following steps:

i) Take schizophrenia patients as the first screening cohort, which includes schizophrenia patients with heart damage and schizophrenia patients without heart damage; through metabolomics research, the screening cohort with heart damage As well as the metabolites in the serum of the screening cohort without heart damage were detected and data analyzed, and the differential metabolites between groups were screened;

ii) Take patients with schizophrenia as the second screening cohort, conduct 24-hour dynamic electrocardiogram detection on the second screening cohort, and divide them into the non-abnormal group and the abnormal group according to the 24-hour dynamic electrocardiogram results; detect the non-abnormal group and the abnormal group respectively The content or concentration of the differential metabolites between the groups in the serum of each group is statistically analyzed, and the screening of the differential metabolites with statistical differences between the groups is the marker for evaluating the cardiac damage in patients with schizophrenia.
The method according to claim 6, characterized in that, in step i), the ratio of the number of patients with schizophrenia with heart damage to the schizophrenia patients without heart damage is 1:0.5-1.5;

In step ii), the ratio of the number of people in the non-abnormal group to the abnormal group is 1:0.5-1.5.
The method according to claim 6, characterized in that, in step i), the method for detecting metabolites is an LC-MS method.
method as claimed in claim 6, is characterized in that, in step i), the method for data analysis is that SIMCA-P software carries out PCA analysis, and carries out the model construction of PLS-DA, OPLS-DA on this basis, thereby Obtain differential metabolites between groups.
The method for assessing the cardiac injury situation of patients with schizophrenia, is characterized in that, described method comprises the following steps:

1) obtaining a serum sample of a subject suffering from schizophrenia, extracting indole-3-lactic acid from the serum sample by means of an extraction reagent, and detecting the concentration of indole-3-lactic acid in the serum;

2) Determine whether the subject suffers from heart damage by the concentration of indole-3-lactic acid in the serum;

Preferably, the method for detecting the concentration of indole-3-lactic acid in serum is selected from the HPLC method or the method of HPLC coupled with mass spectrometry;

Preferably, the concentration of indole-3-lactic acid in the serum is higher than the normal value, which is considered to have cardiac damage.