CN114034851A - Method for predicting prognosis of heart failure patient by using arachidonic acid metabolome data in serum - Google Patents
Method for predicting prognosis of heart failure patient by using arachidonic acid metabolome data in serum Download PDFInfo
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Abstract
The invention relates to a method for predicting the prognosis of a heart failure patient by using arachidonic acid metabolome data in serum, wherein the metabolome products of the arachidonic acid are as follows: 14, 15-DHET: 14, 15-dihydroeyeiosrienoic acid, 14,15-dihydroxyeicosatrienoic acid; 14, 15-EET: 14,15-epoxyeicosatrienoic acid, 14,15-epoxyeicosatrienoic acid; PGD 2: prostaglandin D2, prostaglandin D2; 9-HETE: 9-hydroxyeicosatenoic acid. The prognosis condition of the heart failure patient is predicted as follows: predicting the risk of all-cause death of patients with heart failure within 1 year.
Description
Technical Field
The invention belongs to the technical field of medical treatment, and particularly relates to a method for predicting the prognosis of a heart failure patient by using arachidonic acid metabolome data in serum.
Background
Heart failure (heart failure) is the terminal stage of a variety of cardiovascular diseases including coronary heart disease, myocardial infarction, hypertension, arrhythmias, viral myocarditis, and hereditary cardiomyopathy. Once this occurs, it is almost irreversible. Despite considerable progress in the management of drugs and equipment for heart failure in recent decades, epidemiological investigations have shown that the prevalence of heart failure is still high, approximately 2250 million heart failure patients worldwide, up to 450 million chinese patients, approximately 300 million hospitalizations for heart failure per year, and the prognosis is poor, with 50% 5-year mortality and over 90% 10-year mortality of patients. Furthermore, the prevalence of heart failure increases with age, with less than 2% in patients under 60 years of age and more than 10% in patients over 75 years of age. Therefore, the method can accurately identify the high-risk patients with heart failure, realize early warning and monitoring of the heart failure, explore new treatment strategies and has great significance for prevention and control of the heart failure.
Biomarkers are a quantifiable, cost-effective, convenient and quick tool for identifying potential ways to predict adverse outcome of heart failure. In heart failure, biomarkers provide prognostic value that exceeds that of clinical or imaging tests, such as natriuretic peptides. Heterogeneity of heart failure suggests that assessing multiple biomarkers reflecting different pathophysiological pathways may better explain heart failure. According to the main pathophysiological pathways they represent, the current biomarkers are divided into the following categories: myocardial stretch/stress (i.e., natriuretic peptides), myocardial cell injury/death (i.e., troponin), myocardial fibrosis (i.e., galectin-3), neurohumoral activation (i.e., copeptin), renal insufficiency (i.e., LCN2), and the like. However, to elucidate the complex pathophysiological mechanisms of heart failure to improve risk prediction, biomarkers for other pathways need to be explored.
Arachidonic Acid (AA) is present in all mammalian cells and is one of the most abundant polyunsaturated fatty acids. AA derives a class of metabolites with diverse structural and signaling functions as well as diverse biological roles. Although experimental evidence suggests that AA and its metabolites are involved in a variety of pathological processes of heart failure, including lipid metabolism, inflammatory responses, oxidative stress, and cardiomyocyte apoptosis, etc., clinical data on the potential prognostic value of AA metabolites in heart failure patients remain limited. Previous studies have shown that low eicosapentaenoic acid to AA ratios are associated with higher mortality rates in hospitalized heart failure patients. In another study, lower dihomo-gamma-linolenic acid to AA ratios can predict long-term mortality in patients with acute decompensated heart failure. A thorough understanding of the association of AA metabolites with heart failure mortality may help to identify a subset of patients with a high risk of death. Furthermore, interfering with AA production or signaling may be beneficial for the treatment of high risk patients.
Therefore, the method has high clinical significance and value by exploring the prognosis value of the AA metabolite in the heart failure patients and further discovering new treatment targets.
Disclosure of Invention
The invention relates to an application of a group of serological markers in preparing a detection kit for detecting the prognosis condition of a heart failure patient, wherein the serological markers are as follows: metabolites of arachidonic acid; preferably, the arachidonic acid metabolite is:
14, 15-DHET: 14, 15-dihydroeyeiosrienoic acid, 14,15-dihydroxyeicosatrienoic acid;
14, 15-EET: 14,15-epoxyeicosatrienoic acid, 14,15-epoxyeicosatrienoic acid;
PGD 2: prostaglandin D2, prostaglandin D2;
9-HETE: 9-hydroxyeicosatenoic acid.
The detection of the prognosis condition of the heart failure patient comprises the following steps: patients with heart failure were classified as: a high risk group with all-cause death occurring within 1 year, or a low risk group with all-cause death occurring within 1 year.
Specifically, when the quantitative value of the serological detection marker is:
14, 15-DHET: higher than 0.44 ng/mL;
and/or 14, 15-EET: less than 0.37 ng/mL;
and/or PGD 2: higher than 0.26 ng/mL;
and/or 9-HETE: above 0.85 ng/mL;
the patients are high risk patients with all-cause death within 1 year.
The detection kit comprises reagents for quantifying the serological marker, and the quantifying reagents comprise but are not limited to: enzyme-linked immunosorbent assay reagent, colloidal gold reagent, chemiluminescence reagent, flow-type fluorescence quantitative reagent and mass spectrum quantitative reagent;
preferably, the detection kit is a detection kit for detecting and quantifying the serological marker by mass spectrometry.
The invention also relates to the application of the detection kit in further preparing a combined heart failure prognosis detection product, wherein the combination is as follows:
(1) in combination with a BNP detection kit;
or (2) in combination with an ADHERE model-based detection kit;
or (3) in combination with an optimix-HF model-based detection kit;
or (4) combined with a GWGG-HF model-based detection kit.
The beneficial effect of the invention is that,
1. according to the invention, a large-queue patient sample is monitored for a long time, and a serological metabolite group is screened by single-factor and multi-factor COX regression analysis, so that a group of arachidonic acid metabolites can be used as an evaluation index of 1-year prognosis risk of heart failure patients;
2. the detection limits, cutoff values and the like of the four detection markers are confirmed through a statistical scoring model;
3. through the analysis combined with the existing prognosis model, the four detection markers can be effectively combined with the existing model, so that a better prognosis detection result is obtained;
4. the samples are selected from Chinese population, and consistent results are obtained in the experiment queue and the verification queue.
Drawings
Figure 1, patient enrollment and follow-up experimental flow chart.
Figure 2, a graph of the predicted value of the levels of 20 AA metabolites in serum of patients in cohort for their incidence of 1 year all-cause mortality using single-and multi-factor COX regression analysis.
Figure 3 is a graph of the predictive value scores of 20 AA metabolites analyzed in a discovery cohort for the incidence of 1 year all-cause mortality in patients using the elastic network algorithm.
FIG. 4 is a graph of the results of patients' all-cause death events predicted in a validation cohort using a scoring model constructed from data or ratios of 4 arachidonic acid metabolites (14,15-DHET, 14,15-DHET/14,15-EET, PGD2, 9-HETE). The result shows that patients with the score model cutoff value as a distinction and the model calculation result of more than or equal to the cutoff value have the all-cause death probability greatly increased within 12 months compared with the patients with the model calculation result of less than the cutoff value.
Fig. 5, mass spectrometric detection profiles of serum samples of typical patients in different cohorts (outcome event/non-outcome event), fig. 5A, 14,15-DHET detection results; FIG. 5B, 14,15-EET test results; FIG. 5C, PGD2 shows the results of the test; FIG. 5D, 9-HETE detection results.
Detailed Description
Example 1 group patient screening, serum sample processing and arachidonic acid metabolite detection
Patients with heart failure (n 805) who were continuously admitted were selected to carry out a clinical registration study (clinical trials. gov Identifier: NCT04108182), which was divided into a discovery cohort (n 419) and a verification cohort (n 386), (all participants provided written informed consent, this study was approved by the ethical committee of the beijing ligustrum hospital, ethical number: KS2019017) to detect the levels of baseline serum AA metabolites at which patients were admitted, and each patient was followed up for 1 year. Study ofThe final event is 1 year Death due to disease. The discovery and validation cohorts had 94 and 90 patients with an outcome event, respectively. The flow chart of the patient grouping and follow-up experiment is shown in figure 1.
Serum AA detection in patients enrolled:
1. sample preparation
Serum was extracted using Solid Phase Extraction (SPE). Before extraction, the Waters-Oasis HLB cassette was washed with methanol (1mL) and Milli-Q water (1 mL). The samples were added to the isotope mixtures (5 ng each) and loaded into cassettes. The kit was washed with 1mL of 5% methanol. The water plug was pulled from the SPE cartridge under high vacuum and further dried under high vacuum for 20 minutes. The analyte was eluted into the cartridge with 1mL of methanol. The eluate was then evaporated to dryness.
2. Ultra-high performance liquid chromatography
The chromatographic separation involved the use of a UPLC BEH C18 chromatographic column (1.7 μm, internal diameter 100 × 2.1mm) consisting of ethylene-bridged hybrid particles (Waters, Milford, MA). The column was maintained at 25 ℃ and the injection volume was set to 10. mu.L. Solvent A is water and solvent B is acetonitrile. The flow rate of the mobile phase was 0.6 mL/min. The chromatography was optimized to isolate 32 AA metabolites within 9 min. Gradient from 30% to 40% B for 0-1.5 min; 1.5-6.5min to 60% B; 6.5-7.6min to 80% B, keeping for 1 min; reducing to 30% B in 8.6-8.8min and maintaining for 0.2 min.
3. Mass spectrometry
Targeted preparation of AA metabolites a 5500QTRAP hybrid triple quadrupole linear ion trap mass spectrometer (AB Sciex, Foster City, CA) equipped with a turbo-ion spray electrospray ionization source was used. The mass spectrometer was operated using Analyst 1.5.1 software. The analyte is detected by MRM scanning in negative mode. The residence time used for all MRM experiments was 25 ms. The ion source parameters are CUR 40psi, GS1 30psi, GS2 30psi, IS 4500V, CAD MEDIUM, TEMP 500 ℃.
Example 2 analysis of serum AA metabolites in patients with Heart failure
1. One-and multifactorial COX regression analysis
According to follow-up results, single-factor and multi-factor COX regression analysis finds the prediction value of the level of the AA metabolite in the queue on the 1-year all-cause mortality of heart failure patients. As the results in fig. 2 show, some AA metabolites and metabolite ratios have significant predictive value for 1 year all-cause death in heart failure patients.
2. Score model was constructed and validated with 4 arachidonic acid metabolites or ratios
The mass spectrometric profiles of a typical patient serum sample [ all-cause death event patients (with outcome events) and no event patients (without outcome events) ] are shown in fig. 5, in particular: the results of 14,15-DHET detection are shown in FIG. 5A, the results of 14,15-EET detection are shown in FIG. 5B, PGD2, the results of 9-HETE detection are shown in FIG. 5C, and the results of 9-HETE detection are shown in FIG. 5D.
The chemical names of the 4 markers are:
14, 15-DHET: 14, 15-dihydroeyeiosrienoic acid, 14,15-dihydroxyeicosatrienoic acid;
14, 15-EET: 14,15-epoxyeicosatrienoic acid, 14,15-epoxyeicosatrienoic acid;
PGD 2: prostaglandin D2, prostaglandin D2;
9-HETE: 9-hydroxyeicosatenoic acid.
A scoring model consisting of 4 arachidonic acid metabolites or ratios, AA scoring, was established in the discovery cohort using the elastic network algorithm, and the results are shown in FIG. 3 and validated in the validation cohort.
The results are shown in fig. 4 (the values of Cutoff are the maximum values of the jotan index, the jotan index is sensitivity + specificity-1, and the Cutoff is 1.17 in the figure), and using the maximum value of the jotan index as the cut-off point, the K-M curve analysis shows that the scoring model has the potential to distinguish between 1-year death and non-death in both the discovery and validation cohorts. The mass spectrometric data of the four metabolites, 14,15-DHET, 14,15-EET, PGD2 and 9-HETE, are shown in Table 1 below.
TABLE 1 analysis of the values of concentration detected for 4 AA metabolites in two cohort of patients
In the table, the mann whiney U test was used to compare the concentrations of 4 AA metabolites determined by LC-MS/MS, and was grouped by death status in the discovery queue (n ═ 419) and the validation queue (n ═ 386), respectively. P <0.05 was considered significant.
In the table, the four AA metabolite concentrations are expressed as: median (quartile), unit ng/mL.
Comparing the AA scoring model with BNP and existing clinical risk scores.
The discovery group is used as a training set to summarize the key factors and the calculation formula of the embodiment, the formula is applied to the verification group, the accuracy between the formula prediction result and the clinical follow-up result is analyzed, and as shown in tables 2.1 and 2.2, the AA score can remarkably improve the classification capability of BNP and the existing clinical risk score on 1-year death of heart failure patients.
TABLE 2.1 scoring Table (discovery cohort) with AA (arachidonic acid metabolite) as parameter for predicting all-cause death in 1 year in patients with heart failure
TABLE 2.2 scoring table (validation cohort) with AA (arachidonic acid metabolite) as the prediction parameter for all-cause death in 1 year for patients with heart failure
In tables 2.1 and 2.2,
the column ML-based AA score is the score of four arachidonic acid metabolite scoring models established by the present invention alone;
BNP is the score of the detection result of the commonly used blood marker in the field of single use of cardiovascular;
three columns of ADHERE, OPTIMIZE-HF, GWTG-HF are scores for the test results of three clinical risk score models established in the previous report, taken alone: specifically, the method comprises the following steps:
ADHERE＝Acute Decompensated Heart Failure National Registry；
OPTIMIZE-HF＝Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure；
GWTG-HF＝Get With the Guidelines Heart Failure；
the column of + ML-based AA score is the score used in combination with the BNP, ADHERE, OPTIMIZE-HF, GWGG-HF models by the four arachidonic acid metabolite scoring models established according to the present invention;
NRI ═ net retrieval improvement; the evaluation score is used for analyzing the improvement condition of the past model.
The delta AUC and NRI are used for evaluating the comparison between newly established ML-based AA sore and three previous clinical risk scores in combination with three separate previous scores, and the P values are all less than 0.001, so that the prognosis differentiation efficiency is better than that of the separate clinical scores when the score model established by the invention and composed of four arachidonic acid metabolites or ratios is combined with the previous scores.
Finally, it should be noted that the above embodiments are only used to help those skilled in the art understand the essence of the present invention, and are not used to limit the protection scope of the present invention.
Claims (5)
1. The application of a group of serological markers in preparing a detection kit for detecting the prognosis condition of a heart failure patient comprises the following serological markers: metabolites of arachidonic acid; preferably, the arachidonic acid metabolite is:
14, 15-DHET: 14, 15-dihydroeyeiosrienoic acid, 14,15-dihydroxyeicosatrienoic acid;
14, 15-EET: 14,15-epoxyeicosatrienoic acid, 14,15-epoxyeicosatrienoic acid;
PGD 2: prostaglandin D2, prostaglandin D2;
9-HETE: 9-hydroxyeicosatenoic acid.
2. The test kit according to claim 1, wherein the prognosis for the patient with heart failure is as follows: patients with heart failure were classified as: a high risk group with all-cause death occurring within 1 year, or a low risk group with all-cause death occurring within 1 year.
3. The test kit according to claim 1 or 2, wherein when the quantitative values of the serological test markers are:
14, 15-DHET: higher than 0.44 ng/mL;
and/or 14, 15-EET: less than 0.37 ng/mL;
and/or PGD 2: higher than 0.26 ng/mL;
and/or 9-HETE: above 0.85 ng/mL;
(ii) differentiating the patient into a high risk group with all-cause death within 1 year; otherwise, the low risk group.
4. The test kit according to any one of claims 1 to 3, wherein the test kit comprises: reagents for quantifying the serological marker, including but not limited to: enzyme-linked immunosorbent assay reagent, colloidal gold reagent, chemiluminescence reagent, flow-type fluorescence quantitative reagent and mass spectrum quantitative reagent; preferably, the detection kit is a detection kit for detecting and quantifying the serological marker by mass spectrometry.
5. Use of a test kit according to any one of claims 1 to 4 for the preparation of a combined heart failure prognosis test product, said combination being:
(1) in combination with a BNP detection kit;
or (2) in combination with an ADHERE model-based detection kit;
or (3) in combination with an optimix-HF model-based detection kit;
or (4) combined with a GWGG-HF model-based detection kit.
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| PCT/CN2022/132026 WO2023083380A1 (en) | 2021-11-15 | 2022-11-15 | Method for predicting prognosis of patient with heart failure with arachidonic acid metabolome data in serum |
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| CN117192001A (en) * | 2023-11-03 | 2023-12-08 | 中国中医科学院医学实验中心 | Method for detecting linoleic acid full-pathway metabolites in serum sample |
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| CN117192001B (en) * | 2023-11-03 | 2024-03-08 | 中国中医科学院医学实验中心 | Method for detecting linoleic acid full-pathway metabolites in serum sample |
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