Application of metabolic marker in preparation of product for monitoring heart failure and product

Abstract

The metabolic marker can be used for early diagnosis of heart failure, has the advantages of high sensitivity, rapidness, convenience and accurate and reliable result, provides a basis for clinical decision, provides a certain basis for subsequent fundamental research and clinical research, and has potential application and research values. The metabolic marker comprises one or more of 1-(4-nitrophenyl) piperidine, sphingolipid d19: 3 / 23: 0, phosphatidyl ethanolamine 16: 1e / 22: 5, phosphatidyl ethanolamine 16: 1e / 22: 6, sphingolipid d14: 0 / 22: 1, 3-phosphoglyceric acid, phosphatidylcholine 15: 0 / 20: 4, phosphatidylcholine o-16: 1 / 18: 0, phosphatidylcholine 14: 0e / 20: 1, phosphatidylcholine 17: 0 / 18: 5, betaine, phosphatidylcholine 15: 0 / 18: 2 and phosphatidylcholine 18: 5e / 20: 4.

Description

Technical Field

[0001] The present invention relates to the technical field of biomedical detection, and in particular to the application of a metabolic biomarker in the preparation of a product for monitoring heart failure, and a product capable of effectively reducing the content of a metabolic biomarker composition, which is used for adjuvant treatment of heart failure. Background Art

[0002] Heart failure (HF) is mainly characterized by impaired cardiac pumping function and difficulty in meeting the metabolic needs of surrounding tissues, and is the end-stage state of various cardiovascular diseases clinically. The global prevalence of HF continues to increase, exceeding 64.3 million, and the number of patients in China reaches 13.7 million. Despite the progress in treatment, the prognosis of HF is still poor, the 5-year mortality rate exceeds 50%, and it cannot be completely cured. The 5-year readmission rate is as high as 80%, resulting in a heavy medical burden. Currently, China faces major challenges in the prevention and control of HF.

[0003] The diagnosis of heart failure faces challenges. In particular, the biomarker natriuretic peptide (BNP and NT-proBNP) may be normal in heart failure in specific situations such as obese patients, and the diagnostic cut-off values for atrial fibrillation patients are not unified. Although new biomarkers such as soluble tumor suppressor-2, matrix metalloproteinase, C-reactive protein, growth differentiation factor-15, etc. have potential, their specificity is low due to the influence of various diseases. Therefore, there is an urgent need to find new heart failure biomarkers and establish a sensitive and specific diagnostic model for early risk prediction.

[0004] Metabolomics is an important tool for biomarker discovery, which studies the dynamic changes of small molecule metabolites (<1000 Da, such as amino acids, organic acids, etc.). These small molecules are not only the products of biochemical reactions, but also have a variety of physiological functions and constitute a complex metabolic network. The metabolic network is located downstream of the gene, transcription, and protein networks, reflects their comprehensive effects and exerts feedback, and is highly correlated with the biological phenotype. Metabolomics can also reveal the association between the extracellular environment and disease progression. In short, metabolomics is a key part of the study of biological systems and provides a new perspective for life and medical sciences. Summary of the Invention

[0005] To overcome the defects of the prior art, the technical problem to be solved by the present invention is to provide an application of a metabolic biomarker in the preparation of a product for monitoring heart failure, which can be used for the early diagnosis of heart failure, has high sensitivity, is fast, convenient, and the results are accurate and reliable, can provide a basis for clinical decision-making, and at the same time provide a certain basis for subsequent basic research and clinical research, and has potential application and research value.

[0006] The technical solution of the present invention is: the application of such metabolic markers in the preparation of products for monitoring heart failure, wherein the metabolic markers include one or more of 1-(4-nitrophenyl)piperidine, sphingolipid d19:3 / 23:0, phosphatidylethanolamine 16:1e / 22:5, phosphatidylethanolamine 16:1e / 22:6, sphingolipid d14:0 / 22:1, 3-phosphoglyceric acid, phosphatidylcholine 15:0 / 20:4, phosphatidylcholine o-16:1 / 18:0, phosphatidylcholine 14:0e / 20:1, phosphatidylcholine 17:0 / 18:5, betaine, phosphatidylcholine 15:0 / 18:2, phosphatidylcholine 18:5e / 20:4.

[0007] The metabolic markers of the present invention can be used for the early diagnosis of heart failure, with high sensitivity, rapidity, convenience, and accurate and reliable results, which can provide a basis for clinical decision-making, and at the same time provide a certain foundation for subsequent basic research and clinical research, and have potential application and research value.

[0008] There is also provided a product of the metabolic marker in the preparation of a product for monitoring heart failure, wherein the product is a composite product that reduces the diagnostic model probability of the metabolic marker in plasma, and the product is a food, probiotic preparation or pharmaceutical preparation. Description of the Drawings

[0009] Figure 1 It shows the basic flow chart of the metabolomics study of heart failure of the present invention.

[0010] Figure 2 It shows the detection results of the high-resolution mass spectrometry of the samples in Example 1 of the present invention.

[0011] Among them, Figure 2 A is the total ion current of the positive ion full scan obtained by the high-resolution mass spectrometry detection of the sample; Figure 2 B is the total ion current of the negative ion full scan obtained by the high-resolution mass spectrometry detection of the sample.

[0012] Figure 3 It shows the multivariate statistical model diagrams of the plasma samples in Examples 1 and 2 of the present invention. Among them, Figure 3 A is the score diagram of the discovery set PCA model, Figure 3 B is the score diagram of the discovery set OPLS-DA model.

[0013] Among them, the red circles represent the healthy control group, and the green circles represent heart failure patients. The results show that there can be a good distinction between HC (healthy control) and heart failure patients; Figure 3 C is the score diagram of the validation set PCA model, Figure 3D is the score plot of the OPLS-DA model for the validation set. Among them, the red circles represent the healthy control group, and the green circles represent heart failure patients. The results show that there is a good distinction between HC (healthy control) and heart failure patients.

[0014] Figure 4 Shown are the area under the ROC curve graph and the bar graph of the markers between the healthy controls and heart failure patients in the discovery set in Example 1 of the present invention.

[0015] Figure 5 Shown are the area under the ROC curve graph and the bar graph of the markers between the healthy controls and heart failure patients in the validation set in Example 2 of the present invention.

[0016] Figure 6 Shown is the ROC curve graph and the AUC analysis result of the fitting diagnostic model of the common metabolic markers obtained by Logistic regression analysis in Example 3 of the present invention. Detailed implementation manners

[0017] The application of such metabolic markers in the preparation of products for monitoring heart failure, wherein the metabolic markers include one or more of 1-(4-nitrophenyl)piperidine, sphingolipid d19:3 / 23:0, phosphatidylethanolamine 16:1e / 22:5, phosphatidylethanolamine 16:1e / 22:6, sphingolipid d14:0 / 22:1, 3-phosphoglyceric acid, phosphatidylcholine 15:0 / 20:4, phosphatidylcholine o-16:1 / 18:0, phosphatidylcholine 14:0e / 20:1, phosphatidylcholine 17:0 / 18:5, betaine, phosphatidylcholine 15:0 / 18:2, phosphatidylcholine 18:5e / 20:4.

[0018] The metabolic markers of the present invention can be used for the early diagnosis of heart failure, with high sensitivity, fast and convenient, accurate and reliable results, can provide a basis for clinical decision-making, and at the same time provide a certain basis for subsequent basic research and clinical research, and have potential application and research value.

[0019] Preferably, for the analysis method of the metabolic markers, a fitted regression curve is obtained through Logistic regression analysis:

[0020] Y = Logit(p) = –1.16a – 0.48b – 0.40c – 0.37d – 1.06e + 0.26

[0021] Among them, Y is the diagnostic probability, Logit(p) is the logistic regression function, and a-e are betaine, phosphatidylcholine 14:0e / 20:1, phosphatidylcholine 15:0 / 18:2, phosphatidylcholine 15:0 / 20:4, sphingolipid d14:0 / 22:1 respectively.

[0022] Preferably, the biological sample is plasma, and the level of metabolic markers in the biological sample is detected by one or more of the following methods: chromatography, spectrometry, mass spectrometry, chemical analysis, and immunoassay.

[0023] Preferably, the chromatography includes high performance liquid chromatography, thin layer chromatography, and gas chromatography; the spectrometry includes nuclear magnetic resonance spectrometry, refractive index spectrometry, ultraviolet spectrometry, and near-infrared spectrometry; the chemical analysis includes electrochemistry analysis and radiochemical analysis.

[0024] Preferably, in the chromatography, the mobile phase: mobile phase A is an aqueous solution containing 0.1% formic acid and 2.5 mmol / L ammonium formate, and mobile phase D is acetonitrile; the gradient elution program for sample determination: 0 - 1.0 min, 95% A; 1.0 - 5.0 min, 95% - 40% A; 5.0 - 8.0 min, 40% - 0% A; 8.0 - 11.0 min, 0% A; 11.0 - 14.0 min, 0% - 40% A; 14.0 - 15.0 min, 40% - 95% A; 15.0 - 18.0 min, 95% A, the analysis time is 0 - 18 min, 5 μL is injected each time, the flow rate is 0.25 mL / min, chromatographic column: ACQUITY BEH C18 1.7 μm, 2.1×50 mm, and the chromatographic column temperature is 30 °C.

[0025] Preferably, the mass spectrometry is high-resolution mass spectrometry. First, gradient elution is performed using a chromatographic column, and then data is collected in the positive and negative ion Full scan-ddMS2 mode of the electrospray ionization source ESI.

[0026] Preferably, in the mass spectrometry, the spray voltage: 3000 V; the evaporation temperature: 350 °C; the capillary temperature: 350 °C; S-lens RF: 50; the resolution of the first-stage full scan: 70000, the scanning range: 70 - 1050 m / z; the second-stage data-dependent scan: resolution: 17500, AGC target: 1e5, MaximunTT: 50 ms, NCE: 20, 40, 60.

[0027] Preferably, the pretreatment method of the biological sample before detection is: adding 20 μL of plasma to 180 μL of a precipitating agent containing an internal standard. The internal standard is dissolved in methanol and acetonitrile mixed in equal proportions, vortexed and mixed evenly for 30 s, centrifuged at 12000 rpm for 10 min, and the supernatant is aspirated to obtain a test solution for quantitative analysis.

[0028] There is also provided a preparation of a metabolic marker for use in a product for monitoring heart failure. The preparation is a composite preparation that reduces the probability of the diagnostic model of the metabolic marker in plasma, and the product is a food, a probiotic preparation, or a pharmaceutical preparation.

[0029] Preferably, the diagnostic model probabilities of metabolic markers in plasma before and after the intervention of candidate foods, probiotic preparations or pharmaceutical preparations are detected, and screening is carried out based on whether the probability decreases.

[0030] The embodiments of the present invention will be described in detail below.

[0031] Example 1

[0032] Discovery set: Screening of metabolic markers between plasma of heart failure patients and healthy individuals:

[0033] (I) Sample source:

[0034] After approval by the Ethics Committee of China-Japan Friendship Hospital, plasma samples of 44 HC (healthy controls) and 42 heart failure patients were collected as the discovery set. All participants were from China-Japan Friendship Hospital and were diagnosed with HF by at least two experienced cardiologists according to the 2021 ESC heart failure guidelines. Inclusion criteria: 1. Age ≥ 18 years old; 2. Symptomatic heart failure, New York Heart Association (NYHA) class II-IV; 3. Elevated natriuretic peptide levels: BNP ≥ 100 pg / mL or NT-proBNP ≥ 300 pg / mL. The age and gender of HC were matched with those of heart failure patients to exclude metabolic differences caused by gender and age. The blood sampling time was in the early morning fasting state. All samples were stored at -80 °C for later use.

[0035] (II) Main reagents:

[0036] LC / MS grade acetonitrile was purchased from Merck, HPLC grade methanol was purchased from Merck, and formic acid was purchased from CNW. Other reagents were all commercially available analytical grade. Deionized water was prepared by the Milli-Q ultrapure water system of Millipore.

[0037] (III) Screening of plasma differential metabolites by high-resolution mass spectrometry:

[0038] 3.1 Sample preparation:

[0039] Sample pretreatment: Pipette 20 μL of plasma (healthy control group and heart failure group) and add 180 μL of precipitant containing internal standard (methanol:acetonitrile = 1:1), vortex for 60 s, centrifuge at 12000 rpm for 10 min, and pipette 100 μL for metabolomics analysis.

[0040] 3.2 Chromatography / mass spectrometry conditions:

[0041] Detection was performed using a high-resolution mass spectrometer, QE-Orbitrap. Chromatographic mobile phase: Mobile phase A was an aqueous solution containing 0.1% formic acid and 2.5 mmol / L ammonium formate, and mobile phase D was acetonitrile. Gradient elution program for sample determination: 0 - 1.0 min, 95% A; 1.0 - 5.0 min, 95% - 40% A; 5.0 - 8.0 min, 40% - 0% A; 8.0 - 11.0 min, 0% A; 11.0 - 14.0 min, 0% - 40% A; 14.0 - 15.0 min, 40% - 95% A; 15.0 - 18.0 min, 95% A. Analysis time was 0 - 18 min, with an injection volume of 5 μL each time, and a flow rate of 0.25 mL / min. Chromatographic column: ACQUITY BEH C18 1.7 μm, 2.1×50 mm, column temperature was 30°C, and the temperature of the autosampler was maintained at 4°C. Data was collected in both positive and negative ion modes of the electrospray ionization source (ESI). Spray voltage: 3000 V; evaporation temperature: 350°C; capillary temperature: 350°C; S-lens RF: 50; resolution of the first-stage full scan (Full scan): 70000, scan range: 70 - 1050 m / z. Second-stage data-dependent scan (Full MS / dd-MS2): resolution: 17500; AGC target: 1e5; Maximum IT: 50 ms; NCE: 20, 40, 60.

[0042] The results of high-resolution mass spectrometry detection are as Figure 2 shown.

[0043] 3.3 Metabolic pathway analysis:

[0044] MetaboAnalyst 5.0 was used to analyze the differences in endogenous metabolites in the plasma of the healthy control group and the heart failure group, and endogenous metabolites with a VIP value greater than 1 and a P value less than 0.05 were identified. Then, the Pathway Analysis in the MetaboAnalyst 5.0 website was used to analyze the differential metabolic pathways in the plasma, and metabolic pathways with an Impact value greater than 0.1 were selected as the main differential metabolic pathways in the plasma.

[0045] (IV) Data processing and statistical analysis:

[0046] Identification of endogenous metabolites: High-resolution mass spectrometry mzCloud was used to obtain the exact mass number with 5 decimal places for each endogenous metabolite, and it was identified by the molecular formula of each endogenous metabolite. Then, Compound Discover software was used to automatically search the self-built library and publicly available databases on the Internet for metabolite name annotation. Subsequently, the MetaboAnalyst 5.0 website was used to analyze the metabolite matrix, draw PCA and OPLS-DA model diagrams, and find the differences in metabolic patterns and obvious classification trends between healthy people and heart failure patients. Using SPSS software, the group was set as the status variable, and the measured intensity of the target plasma metabolites was set as the test variable. After calculating the ROC curve, SPSS generated an output report containing the ROC curve diagram and the area under the curve (AUC) value. Finally, differential metabolic markers were selected according to the criteria of VIP>1, P<0.05, and ROC>0.7.

[0047] (V) Results:

[0048] PCA and OPLS-DA were calculated using the MetaboAnalyst 5.0 website. PCA( Figure 3 A) The results showed the differences between the plasma of the healthy control group and the plasma of the heart failure group. OPLS-DA( Figure 3 B) The results showed that the plasma metabolites of the healthy control group and the heart failure group could be completely separated. At the same time, endogenous substances with VIP>1, P value less than 0.05, and ROC>0.7 were selected as the main differential metabolites. Table 1 shows the change rates of differential metabolites in plasma obtained using the OPLS-DA model. Figure 4 It is the ROC curve diagram and bar chart of differential metabolites. The results showed that there were a total of 13 differential metabolites in plasma. Compared with the healthy control group, the proportions of 11 endogenous substances in the heart failure group mainly increased, and the other 2 mainly showed a downward trend. The results indicated that after the occurrence of heart failure, it affected the metabolic secretion of plasma endogenous substances, causing obvious changes in their contents, and the main changing components were lipid substances.

[0049] Table 1

[0050] NO Metabolite VIP P value Log2 FC AUC Trend 1 1-(4-Nitrophenyl)piperidine 2.70 5.79E-16 1.06 0.97 Up-regulated 2 Sphingolipid d19:3 / 23:0 1.98 1.55E-11 0.54 0.94 Up-regulated 3 Phosphatidylethanolamine 16:1e / 22:5 2.26 2.87E-14 -0.75 0.93 Down-regulated 4 Phosphatidylethanolamine 16:1e / 22:6 2.04 7.17E-14 -0.72 0.93 Down-regulated 5 Sphingolipid d14:0 / 22:1 1.34 2.44E-07 0.53 0.85 Up-regulated 6 3-Phosphoglyceric acid 1.53 6.99E-06 0.34 0.84 Up-regulated 7 Phosphocholine 15:0 / 20:4 1.45 1.37E-06 0.43 0.83 Up-regulated 8 Phosphocholine o-16:1 / 18:0 1.78 8.46E-09 0.68 0.83 Up-regulated 9 Phosphocholine 14:0e / 20:1 1.72 9.89E-09 0.66 0.82 Up-regulated 10 Phosphocholine 17:0 / 18:5 1.54 2.64E-06 0.44 0.81 Up-regulated 11 Betaine 1.41 0.000177116 0.32 0.73 Up-regulated 12 Phosphocholine 15:0 / 18:2 1.27 0.006060816 0.23 0.73 Up-regulated 13 Phosphocholine 18:5e / 20:4 2.15 0.000437312 0.34 0.73 Up-regulated

[0051] Example 2

[0052] Verification set: Verification of metabolic markers between the plasma of heart failure patients and healthy people

[0053] (I) Sample collection:

[0054] Plasma samples from 56 healthy controls (HC) and 77 heart failure patients were collected as the validation set. All participants were from China-Japan Friendship Hospital. The age and gender of the HC were matched with those of the heart failure patients to exclude metabolic differences caused by gender and age. The blood sampling time was in the early morning on an empty stomach. Fasting plasma samples of the subjects were collected and stored in a -80 °C refrigerator for later use.

[0055] (II) Sample detection and statistical analysis: The same as item (III) and (IV) in Example 1

[0056] (III) Result analysis:

[0057] PCA and OPLS-DA were calculated using the MetaboAnalyst 5.0 website. PCA ( Figure 3 C) The results showed the differences between the plasma of the healthy control group and that of the heart failure group. OPLS-DA ( Figure 3 D) The results showed that the plasma metabolites of the healthy control group and the heart failure group could be completely separated. At the same time, the parameters of 13 differential metabolic markers in the discovery set plasma were calculated in the validation set, as shown in Table 2. Figure 5 ROC curve graph and bar graph of differential metabolites in the validation set. Compared with the healthy control group, the proportions of 11 endogenous substances in the heart failure group mainly increased, and the other 2 mainly showed a downward trend, which was consistent with the trend in the discovery set. The results further indicated that after the occurrence of heart failure, the metabolism and secretion of plasma endogenous substances were affected, resulting in obvious changes in their contents.

[0058] Table 2

[0059]

[0060]

[0061] The ROCs of 8 metabolic markers such as 1-(4-nitrophenyl)piperidine, sphingolipid d19:3 / 23:0, phosphatidylethanolamine 16:1e / 22:5, phosphatidylethanolamine 16:1e / 22:6, 3-phosphoglyceric acid, phosphatidylcholine o-16:1 / 18:0, phosphatidylcholine 17:0 / 18:5, and phosphatidylcholine 18:5e / 20:4 were all greater than 0.7, which was of great significance for the diagnosis and treatment of heart failure.

[0062] Study on the differences in the metabolic pathways of endogenous substances in plasma:

[0063] Table 3 shows the metabolic pathways analyzed using MetaboAnalyst 5.0 for plasma. The results in Table 3 show that there are significantly different metabolic pathways with an Impact value greater than 0.1 between the healthy control group and the heart failure group. There are mainly three differentially expressed metabolic pathways, indicating that the main affected pathways include histidine metabolism, alanine, aspartate, and glutamate metabolism, and glycerophospholipid metabolism. The analysis results show that after the occurrence of heart failure, the metabolism and synthesis of amino acids and lipids are affected, leading to changes in the content of plasma endogenous substances.

[0064] Table 3

[0065] No. Metabolic pathway Total Hits Impact 1 Histidine metabolism 16 1 0.22131 2 Alanine, aspartate and glutamate metabolism 28 1 0.11378 3 Glycerophospholipid metabolism 36 2 0.11201

[0066] Example 3

[0067] Establishment of a heart failure diagnosis model

[0068] (1) Data statistics

[0069] The discovery set and validation set were combined. For healthy subjects and heart failure patients, a diagnostic model was constructed to further determine the 5 common metabolic markers with an ROC < 0.7 in the validation set, including: sphingolipid d14:0 / 22:1, phosphatidylcholine 15:0 / 20:4, phosphatidylcholine 14:0e / 20:1, betaine, phosphatidylcholine 15:0 / 18:2, to determine whether early diagnosis of heart failure can be performed. Logistic regression analysis in SPSS was used to construct the diagnostic model, and the Hosmer-Lemeshow test was used to evaluate the goodness of fit of the model. When P < 0.05, it indicates that the model has a poor fit, and vice versa, the model has a good fit. Using SPSS software, the group was set as the status variable, and the measured intensity of the target plasma metabolite was set as the test variable. After calculating the ROC curve, SPSS generated an output report containing the ROC curve graph and the area under the curve (AUC) value.

[0070] (2) Result analysis

[0071] The regression curve fitted based on 5 metabolic markers was obtained through Logistic regression analysis, that is, the diagnostic model: Y = Logit(p) = -1.16a - 0.48b - 0.40c - 0.37d - 1.06e + 0.26 (Y is the diagnostic probability, Logit(p) is the logistic regression function, and a - e are betaine, phosphatidylcholine 14:0e / 20:1, phosphatidylcholine 15:0 / 18:2, phosphatidylcholine 15:0 / 20:4, sphingolipid d14:0 / 22:1 respectively. After fitting, the area under the ROC curve AUC of the diagnostic model was 0.756 in the comparison between the healthy control group and the heart failure group. Figure 6), with a 95% confidence interval of 0.694 - 0.812, significantly improving the diagnostic efficacy of a single biomarker.

[0072] The Hosmer-Lemeshow test showed that P = 0.97, indicating a good model fit.

[0073] The above are only the preferred embodiments of the present invention and do not impose any formal limitations on the present invention. Any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. Use of a metabolic marker in the preparation of a product for monitoring heart failure, characterized in that: The metabolic markers include one or more of 1-(4-nitrophenyl)piperidine, sphingolipid d19:3 / 23:0, phosphatidylethanolamine 16:1e / 22:5, phosphatidylethanolamine 16:1e / 22:6, sphingolipid d14:0 / 22:1, 3-phosphoglyceric acid, phosphatidylcholine 15:0 / 20:4, phosphatidylcholine o-16:1 / 18:0, phosphatidylcholine 14:0e / 20:1, phosphatidylcholine 17:0 / 18:5, betaine, phosphatidylcholine 15:0 / 18:2, phosphatidylcholine 18:5e / 20:

4.

2. Use of the metabolic marker according to claim 1 in the preparation of a product for monitoring heart failure, characterized in that: For the analysis method of the metabolic markers, the fitted regression curve is obtained through Logistic regression analysis: Y = Logit(p) = –1.16a – 0.48b – 0.40c – 0.37d – 1.06e + 0.26 Among them, Y is the diagnostic probability, Logit(p) is the logistic regression function, and a - e are betaine, phosphatidylcholine 14:0e / 20:1, phosphatidylcholine 15:0 / 18:2, phosphatidylcholine 15:0 / 20:4, sphingolipid d14:0 / 22:1 respectively.

3. Use of the metabolic marker according to claim 2 in the preparation of a product for monitoring heart failure, characterized in that: The biological sample is plasma, and the levels of metabolic markers in the biological sample are detected by one or more of the following methods: chromatography, spectroscopy, mass spectrometry, chemical analysis, immunoassay.

4. Use of the metabolic marker according to claim 3 in the preparation of a product for monitoring heart failure, characterized in that: The chromatography includes high performance liquid chromatography, thin layer chromatography, gas chromatography; the spectroscopy includes nuclear magnetic resonance spectroscopy, refractive index spectroscopy, ultraviolet spectroscopy, near-infrared spectroscopy; the chemical analysis includes electrochemical analysis, radiochemical analysis.

5. Use of the metabolic marker according to claim 3 in the preparation of a product for monitoring heart failure, characterized in that: In the chromatography, the mobile phase: Mobile phase A is an aqueous solution containing 0.1% formic acid and 2.5 mmol / L ammonium formate, and mobile phase D is acetonitrile; the gradient elution program for sample determination: 0 - 1.0 min, 95% A; 1.0 - 5.0 min, 95% - 40% A; 5.0 - 8.0 min, 40% - 0% A; 8.0 - 11.0 min, 0% A; 11.0 - 14.0 min, 0% - 40% A; 14.0 - 15.0 min, 40% - 95% A; 15.0 - 18.0 min, 95% A, the analysis time is 0 - 18 min, the injection volume is 5 μL each time, the flow rate is 0.25 mL / min, the chromatographic column: ACQUITY BEH C18 1.7 μm, 2.1×50 mm, and the column temperature is 30°C.

6. Use of the metabolic marker according to claim 3 in the preparation of a product for monitoring heart failure, characterized in that: The mass spectrometry is high resolution mass spectrometry. First, gradient elution is carried out using a chromatographic column, and then data is collected in the positive and negative ion Full scan-ddMS2 mode of the electrospray ionization source ESI.

7. Use of the metabolic marker according to claim 6 in the preparation of a product for monitoring heart failure, characterized in that: In the mass spectrometry, the spray voltage: 3000 V; the evaporation temperature: 350°C; the capillary temperature: 350°C; S-lens RF: 50; the resolution of the first-stage full scan: 70000, the scanning range: 70 - 1050 m / z; the second-stage data-dependent scan: resolution: 17500, AGC target: 1e5, Maximun TT: 50 ms, NCE: 20, 40, 60.

8. Use of the metabolic marker according to claim 3 in the preparation of a product for monitoring heart failure, characterized in that: The pretreatment method of the biological sample before detection is as follows: 20 μL of plasma is added to 180 μL of precipitating agent containing internal standard. The internal standard is dissolved in methanol and acetonitrile after being mixed in equal proportion, vortexed and mixed evenly for 30 s, centrifuged at 12,000 rpm for 10 min, and the supernatant is aspirated to obtain a test solution for quantitative analysis.

9. The metabolite marker according to claim 1 for use in the preparation of a product for monitoring heart failure, characterized in that: The product is a composite product that reduces the probability of the diagnostic model of the metabolic marker in plasma, and the product is a food, probiotic preparation or pharmaceutical preparation.

10. The metabolite marker according to claim 9, in the preparation of a product for monitoring heart failure, characterized in that: Detect the probability of the diagnostic model of the metabolic marker in plasma before and after the intervention of the candidate food, probiotic preparation or pharmaceutical preparation, and screen with whether the probability decreases as the standard.

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