Use of metabolic markers in the manufacture of a product for monitoring heart failure and products

Abstract

The application and preparation of metabolic biomarkers in the development of products for monitoring heart failure can be used for the early diagnosis of heart failure. These biomarkers are highly sensitive, rapid, convenient, and provide accurate and reliable results, offering a basis for clinical decision-making and laying a foundation for subsequent basic and clinical research. They have potential application and research value. The metabolic biomarkers include one or more of the following: 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, and phosphatidylcholine 18:5e / 20:4.

Description

Technical Field

[0001] This invention relates to the technical field of biomedical detection, and more particularly to the application of a metabolic biomarker in the preparation of products for monitoring heart failure, and an article that can effectively reduce the content of a metabolic biomarker composition for adjunctive treatment of heart failure. Background Technology

[0002] Heart failure (HF) is characterized by impaired cardiac pumping function, making it unable to meet the metabolic demands of peripheral tissues. It is the end-stage condition of various cardiovascular diseases. The global prevalence of HF continues to rise, exceeding 64.3 million, with 13.7 million patients in my country. Despite advances in treatment, the prognosis for HF remains poor, with a 5-year mortality rate exceeding 50%, and it is not completely curable. The 5-year readmission rate is as high as 80%, resulting in a heavy medical burden. Currently, HF prevention and control in my country faces significant challenges.

[0003] Heart failure diagnosis faces challenges, particularly since biomarkers such as natriuretic peptides (BNP and NT-proBNP) may be normal in certain situations, such as in obese patients with heart failure, while the diagnostic cutoff values ​​for atrial fibrillation are inconsistent. Novel biomarkers, such as soluble carcinogen-2, matrix metalloproteinases, C-reactive protein, and growth differentiation factor-15, while promising, suffer from low specificity due to the influence of various diseases. Therefore, there is an urgent need to identify novel heart failure biomarkers and establish sensitive and specific diagnostic models for early risk prediction.

[0004] Metabolomics is an important tool for biomarker discovery, studying the dynamic changes of small molecule metabolites (<1000 Da, such as amino acids and organic acids). These small molecules are not only products of biochemical reactions but also possess multiple physiological functions, forming complex metabolic networks. These metabolic networks are downstream of gene, transcription, and protein networks, reflecting their combined effects and exerting feedback, and are highly correlated with biological phenotypes. Metabolomics can also reveal the link between the extracellular environment and disease progression. In summary, metabolomics is a key part of biological systems research, providing new perspectives for life and medical sciences. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, the technical problem to be solved by this invention is to provide an application of metabolic biomarkers in the preparation of products for monitoring heart failure. This biomarker can be used for the early diagnosis of heart failure, with high sensitivity, speed and convenience, and accurate and reliable results. It can provide a basis for clinical decision-making and also provide a certain foundation for subsequent basic and clinical research, thus having potential application and research value.

[0006] The technical solution of the present invention is: the application of this metabolic biomarker in the preparation of products for monitoring heart failure, wherein the metabolic biomarker includes 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, and phosphatidylcholine 18:5e / 20:4.

[0007] The metabolic biomarkers of this invention can be used for the early diagnosis of heart failure. They are highly sensitive, rapid, convenient, and provide accurate and reliable results, which can provide a basis for clinical decision-making and also provide a foundation for subsequent basic and clinical research, thus having potential application and research value.

[0008] Also provided are articles in which metabolic biomarkers are used in the preparation of products for monitoring heart failure, said articles being composite articles that reduce the probability of diagnostic models of said metabolic biomarkers in plasma, said products being food, probiotic preparations or pharmaceutical preparations. Attached Figure Description

[0009] Figure 1 This is a basic flowchart of the heart failure metabolomics study of this invention.

[0010] Figure 2 The image shows the high-resolution mass spectrometry detection results of the sample in Embodiment 1 of the present invention.

[0011] in, Figure 2 A is the total ion current of the positive ions obtained from the high-resolution mass spectrometry detection of the sample. Figure 2 B is the total ion current of negative ions obtained from high-resolution mass spectrometry detection of the sample.

[0012] Figure 3 The diagram shows the multivariate statistical model of plasma samples in Embodiments 1 and 2 of this invention. Figure 3 A is the PCA model score graph of the discovery set. Figure 3 B is the score graph of the OPLS-DA model in the discovery set.

[0013] In this study, red circles represent healthy control groups and green circles represent patients with heart failure. The results show that healthy control (HC) and patients with heart failure can be well distinguished. Figure 3 C is the PCA model score graph on the validation set. Figure 3D is the validation set OPLS-DA model score graph. Red circles represent healthy controls, and green circles represent heart failure patients. The results show that healthy controls (HC) and heart failure patients can be distinguished relatively well.

[0014] Figure 4 The figures shown are the area under the ROC curve and the bar chart of biomarkers between healthy controls and heart failure patients in the discovery set in Embodiment 1 of the present invention.

[0015] Figure 5 The figures shown are the area under the ROC curve and the bar chart of biomarkers between healthy controls and heart failure patients in the validation set in Embodiment 2 of the present invention.

[0016] Figure 6 The figure shown is the ROC curve and AUC analysis results of the diagnostic model fitted by common metabolic markers obtained by Logistic regression analysis in Embodiment 3 of the present invention. Detailed Implementation

[0017] The application of this metabolic biomarker in the preparation of products for monitoring heart failure, said metabolic biomarker includes 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, and phosphatidylcholine 18:5e / 20:4.

[0018] The metabolic biomarkers of this invention can be used for the early diagnosis of heart failure. They are highly sensitive, rapid, convenient, and provide accurate and reliable results, which can provide a basis for clinical decision-making and also provide a foundation for subsequent basic and clinical research, thus having potential application and research value.

[0019] Preferably, the analytical method for the metabolic biomarkers involves obtaining a fitted regression curve through logistic regression analysis:

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

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

[0022] Preferably, 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, and immunoassay.

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

[0024] Preferably, the mobile phase in the chromatographic method is: 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 is: 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 per sample; the flow rate is 0.25 mL / min; the chromatographic column is ACQUITY BEH C18 1.7 μm, 2.1 × 50 mm; and the column temperature is 30 °C.

[0025] Preferably, the mass spectrometry method is high-resolution mass spectrometry, which first uses a chromatographic column for gradient elution, and then acquires data in the ESI positive and negative ion Full scan-ddMS2 mode.

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

[0027] Preferably, the pretreatment method of the biological sample before detection is as follows: 20 μL of plasma is added to 180 μL of precipitant containing internal standard. The internal standard is dissolved in methanol and acetonitrile mixed in equal proportions. The mixture is vortexed for 30 s, centrifuged at 12000 rpm for 10 min, and the supernatant is collected to obtain the test solution for quantitative analysis.

[0028] Also provided are articles in which metabolic biomarkers are used in the preparation of products for monitoring heart failure, said articles being composite articles that reduce the probability of diagnostic models of said metabolic biomarkers in plasma, said products being food, probiotic preparations or pharmaceutical preparations.

[0029] Preferably, the probability of a diagnostic model of metabolic markers in plasma before and after intervention with candidate foods, probiotic preparations, or drug preparations is detected, and the model is screened 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 biomarkers in the plasma of patients with heart failure and healthy individuals:

[0033] (a) Sample source:

[0034] Following approval from the Ethics Committee of the China-Japan Friendship Hospital, plasma samples were collected from 44 healthy controls (HC) and 42 patients with heart failure as the discovery set. All participants were from the China-Japan Friendship Hospital and diagnosed with heart failure by at least two experienced cardiologists according to the 2021 ESC Heart Failure Guidelines. Inclusion criteria: 1. Age ≥ 18 years; 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. HC participants were matched for age and sex with heart failure patients to exclude metabolic differences due to sex and age. Blood was collected in the morning on an empty stomach. All samples were stored at -80°C until use.

[0035] (II) Main Reagents:

[0036] Acetonitrile (LC / MS grade) and methanol (HPLC grade) were purchased from Merck, and formic acid was purchased from CNW. All other reagents were commercially available analytical grade. Deionized water was prepared using the Milli-Q ultrapure water system from Millipore.

[0037] (III) High-resolution mass spectrometry screening of plasma differential metabolites:

[0038] 3.1 Sample preparation:

[0039] Sample pretreatment: 20 μL of plasma (healthy control group and heart failure group) was added to 180 μL of precipitant containing internal standard (methanol:acetonitrile = 1:1), vortexed for 60 s, centrifuged at 12000 rpm for 10 min, and 100 μL was used for metabolomics analysis.

[0040] 3.2 Chromatographic / Mass Spectrometry Conditions:

[0041] High-resolution mass spectrometry (QE-Orbitrap) was used for detection. The chromatographic mobile phases were: A, an aqueous solution containing 0.1% formic acid and 2.5 mmol / L ammonium formate; and D, acetonitrile. The gradient elution program for sample determination was as follows: 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 was 0-18 min, with 5 μL injected per sample at a flow rate of 0.25 mL / min. The column was an ACQUITY BEH C18 1.7 μm, 2.1 × 50 mm, with a column temperature of 30 °C and the autosampler temperature maintained at 4 °C. Data was acquired using an electrospray ionization (ESI) source in both positive and negative ion modes. Spray voltage: 3000V; evaporation temperature: 350℃; capillary temperature: 350℃; S-lens RF: 50; Level 1 full scan resolution: 70000, scan range: 70-1050 m / z. Level 2 data-dependent scan (Full MS / dd-MS2): resolution: 17500; AGC target: 1e5; Maximum Tachometer: 50 ms; NCE: 20, 40, 60.

[0042] High-resolution mass spectrometry detection results as follows Figure 2 As shown.

[0043] 3.3 Metabolic pathway analysis:

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

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

[0046] The identification of endogenous metabolites was performed using high-resolution mass spectrometry (mzCloud) to obtain the precise mass number of each endogenous metabolite to five decimal places. Each endogenous metabolite was identified by its molecular formula. Compound Discover software was then used to automatically search a self-built library and publicly available online databases for metabolite name annotation. Subsequently, MetaboAnalyst 5.0 was used to analyze the metabolite matrix, plotting PCA and OPLS-DA models to identify differences in metabolic patterns and significant classification trends between healthy individuals and heart failure patients. SPSS software was used to set the groups as state variables and the intensity of target plasma metabolite measurements as test variables. After calculating ROC curves, SPSS generated an output report including the ROC curve and the area under the curve (AUC) value. Finally, differentially expressed metabolic biomarkers were selected based on the criteria of VIP>1, P<0.05, and ROC>0.7.

[0047] (V) Results:

[0048] Calculate PCA and OPLS-DA using the MetaboAnalyst 5.0 website. PCA ( Figure 3 A) Results showed differences between plasma from the healthy control group and the heart failure group, OPLS-DA ( Figure 3 B) The results showed that plasma metabolites were completely separated between the healthy control group and the heart failure group. Endogenous substances with VIP>1, P-value less than 0.05, and ROC>0.7 were selected as the main differentially metabolites. Table 1 shows the rate of change of differentially metabolites in plasma obtained using the OPLS-DA model. Figure 4 The results show the ROC curves and bar charts for the differentially expressed metabolites. A total of 13 differentially expressed metabolites were found in plasma. Compared with the healthy control group, the proportions of 11 endogenous substances in the heart failure group were mainly increased, while the proportions of the other 2 showed a mainly decreasing trend. This indicates that heart failure affects the metabolic secretion of endogenous substances in plasma, causing significant changes in their levels, with lipids being the main component affected.

[0049] Table 1

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

[0051] Example 2

[0052] Validation set: Validation of metabolic markers in the plasma of patients with heart failure and healthy individuals.

[0053] (I) Sample Collection:

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

[0055] (II) Sample testing and statistical analysis: Same as "(III) and (IV)" in Example 1.

[0056] (III) Results Analysis:

[0057] Calculate PCA and OPLS-DA using the MetaboAnalyst 5.0 website. PCA ( Figure 3 C) Results showed differences between plasma from the healthy control group and the heart failure group, OPLS-DA ( Figure 3 D) The results showed that plasma metabolites in the healthy control group and the heart failure group could be completely separated. Simultaneously, 13 differentially expressed metabolic markers in the plasma of the discovery group were parametrically analyzed in the validation set, as shown in Table 2. Figure 5 To validate the ROC curves and bar charts of differentially expressed metabolites, the proportions of 11 endogenous substances in the heart failure group were mainly increased compared to the healthy control group, while the proportions of the other two showed a mainly decreasing trend, consistent with the trend observed in the discovery set. These results further indicate that the occurrence of heart failure affects the metabolic secretion of plasma endogenous substances, causing significant changes in their levels.

[0058] Table 2

[0059]

[0060]

[0061] Among them, the ROC values ​​of eight metabolic markers, including 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, are all greater than 0.7, which is of great significance for the diagnosis and treatment of heart failure.

[0062] Differences in endogenous metabolic pathways in plasma:

[0063] Table 3 shows the metabolic pathways in plasma analyzed using MetaboAnalyst 5.0. The results in Table 3 show that there are significantly different metabolic pathways with impact values ​​greater than 0.1 between the healthy control group and the heart failure group. Three main metabolic pathways showed differences, indicating that the main affected pathways include histidine metabolism, alanine, aspartate, and glutamate metabolism, and glycerophospholipid metabolism. The analysis results show that the occurrence of heart failure affects the metabolism and synthesis of amino acids and lipids, thereby leading to changes in the levels of endogenous substances in plasma.

[0064] Table 3

[0065] No. metabolic pathways Total Hits Impact 1 Histidine metabolism 16 1 0.22131 2 Metabolism of alanine, aspartic acid and glutamate 28 1 0.11378 3 Glycerol phospholipid metabolism 36 2 0.11201

[0066] Example 3

[0067] Establishment of a diagnostic model for heart failure

[0068] (I) Data Statistics

[0069] By merging the discovery and validation sets, and targeting healthy subjects and patients with heart failure, a diagnostic model was constructed to further determine whether five common metabolic biomarkers with ROC < 0.7 in the validation set—including sphingolipid d14:0 / 22:1, phosphatidylcholine 15:0 / 20:4, phosphatidylcholine 14:0e / 20:1, betaine, and phosphatidylcholine 15:0 / 18:2—could be used for the early diagnosis of heart failure. Logistic regression analysis in SPSS was used to construct the diagnostic model, and the Hosmer-Lemeshow test was used to evaluate the model's goodness of fit. A P < 0.05 indicated a poor model fit, while a higher P < 0.05 indicated a good model fit. Using SPSS software, groups were set as state variables, and the intensity of target plasma metabolite measurements was used as the test variable. After calculating the ROC curve, SPSS generated an output report containing the ROC curve and the area under the curve (AUC) value.

[0070] (II) Results Analysis

[0071] Logistic regression analysis yielded a regression curve based on five metabolic biomarkers, forming the diagnostic model: Y = Logit(p) = -1.16a - 0.48b - 0.40c - 0.37d - 1.06e + 0.26 (where Y is the diagnostic probability, Logit(p) is the logistic regression function, and ae represent betaine, phosphatidylcholine 14:0e / 20:1, phosphatidylcholine 15:0 / 18:2, phosphatidylcholine 15:0 / 20:4, and sphingolipid d 14:0 / 22:1, respectively). The area under the ROC curve (AUC) of the diagnostic model was 0.756 when comparing the healthy control group and the heart failure group after fitting. Figure 6The 95% confidence interval was 0.694-0.812, which significantly improved the diagnostic efficacy of a single biomarker.

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

[0073] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. The application of metabolic biomarkers in the preparation of products for monitoring heart failure, characterized in that: The metabolic biomarkers 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-phosphoglycerate, phosphatidylcholine 15:0 / 20:4, phosphatidylcholine o-16:1 / 18:0, phosphatidylcholine 14:0e / 20:1, phosphatidylcholine 17:0 / 18:5, phosphatidylcholine 15:0 / 18:2, and phosphatidylcholine 18:5e / 20:4, and the biological sample for the metabolic biomarkers is plasma.

2. The application of the metabolic biomarker according to claim 1 in the preparation of products for monitoring heart failure, characterized in that: The analytical method for the metabolic biomarkers, through logistic regression analysis, yields a fitted regression curve: Y=Logit(p)=–1.16a–0.48b–0.40c–0.37d–1.06e+0.26 Where Y is the diagnostic probability, Logit(p) is the logistic regression function, and ae are betaine, phosphatidylcholine 14:0e / 20:1, phosphatidylcholine 15:0 / 18:2, phosphatidylcholine 15:0 / 20:4, and sphingolipid d14:0 / 22:1, respectively.

3. The application of the metabolic biomarker according to claim 2 in the preparation of products for monitoring heart failure, characterized in that: The levels of metabolic markers in biological samples can be detected by one or more of the following methods: chromatography, spectroscopy, mass spectrometry, chemical analysis, and immunoassay.

4. The application of the metabolic biomarker according to claim 3 in the preparation of products for monitoring heart failure, characterized in that: The chromatographic methods include high performance liquid chromatography, thin-layer chromatography, and gas chromatography; the spectroscopic methods include nuclear magnetic resonance spectroscopy, refractive index spectroscopy, ultraviolet spectroscopy, and near-infrared spectroscopy; and the chemical analysis methods include electrochemical analysis and radiochemical analysis.

5. The application of the metabolic biomarker according to claim 4 in the preparation of products for monitoring heart failure, characterized in that: In the high-performance liquid chromatography method described above, the mobile phases are: A, which is an aqueous solution containing 0.1% formic acid and 2.5 mmol / L ammonium formate; and D, which is acetonitrile. The gradient elution program for sample determination is: 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; 11.0-14.0min, 0%-40%A; 14.0-15.0min, 40%-95%A; 15.0-18.0 min, 95% A, analysis time 0-18 min, 5 μL injection per sample, flow rate 0.25 mL / min, column: ACQUITYBEH C18 1.7 μm, 2.1 × 50 mm, column temperature 30 ℃.

6. The application of the metabolic biomarker according to claim 3 in the preparation of products for monitoring heart failure, characterized in that: The mass spectrometry method described is high-resolution mass spectrometry, which first uses a chromatographic column for gradient elution, and then acquires data in the ESI positive and negative ion Full scan-ddMS2 mode using an electrospray ionization source.

7. The application of the metabolic biomarker according to claim 6 in the preparation of products for monitoring heart failure, characterized in that: In the mass spectrometry method described, the spray voltage was 3000V; the evaporation temperature was 350℃; the capillary temperature was 350℃; the S-lens RF was 50; the resolution of the first-stage full scan was 70000, and the scan range was 70-1050 m / z; the second-stage data-dependent scan had the following parameters: resolution 17500, AGC target 1e5, Maximum TT 50ms, and NCE 20, 40, and 60.

8. The application of the metabolic biomarker according to claim 3 in the preparation of products for monitoring heart failure, characterized in that: The pretreatment method for the biological samples before detection is as follows: 20 μL of plasma is added to 180 μL of precipitant containing internal standard. The internal standard is dissolved in methanol and acetonitrile mixed in equal proportions. The mixture is vortexed for 30 s, centrifuged at 12000 rpm for 10 min, and the supernatant is collected to obtain the test solution for quantitative analysis.

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