Application of biomarker in ejection fraction retention type heart failure detection

Abstract

The invention relates to the technical field of medical biology, and discloses an application of a biomarker in the diagnosis of ejection fraction retention type heart failure, and the biomarker is kynuric acid. The biomarker is applied to preparation of a product for diagnosing or predicting ejection fraction retention heart failure. According to the method, the ejection fraction retention heart failure can be diagnosed by quantitatively detecting the kynuric acid in the plasma.

Description

Technical Field

[0001] This invention relates to the field of medical biotechnology, specifically to the application of a biomarker in the preparation of products for diagnosing or predicting heart failure with preserved ejection fraction. Background Technology

[0002] Heart failure (HF) is pathophysiologically defined as the heart's inability to effectively pump blood to meet the body's needs. Its main clinical manifestations are typical symptoms and signs caused by elevated cardiac filling pressure at rest or during exertion, including fluid retention, dyspnea, fatigue, and exercise intolerance. Currently, based on left ventricular ejection fraction (LVEF), clinically, heart failure with preserved ejection fraction (HFpEF) is defined as LVEF > 50%, and heart failure with reduced ejection fraction (HFrEF) is defined as LVEF < 40%. A report from the Heart Failure Association of America indicates that the lifetime risk of HF is approximately 24%, meaning that about one-quarter of people will develop HF in their lifetime. With the accelerating aging of the population and the rising incidence of metabolic-related diseases (such as obesity and diabetes) caused by unhealthy lifestyles, the prevalence of HFpEF is increasing year by year and has become a major type of heart failure.

[0003] Heart failure with ventricular ejection fraction (HFpEF) is a highly complex clinical syndrome. Its early symptoms and signs lack specificity, making early diagnosis challenging. Currently, the gold standard for HFpEF diagnosis still relies on invasive right ventricular catheterization for hemodynamic assessment; however, the widespread adoption of this invasive diagnostic method in clinical practice is limited. The current diagnosis of HFpEF is mainly based on a comprehensive assessment of clinical manifestations, echocardiography, and natriuretic peptide levels, but both sensitivity and specificity are low. Traditional echocardiography relies on measurements at rest, while in some HFpEF patients, left ventricular filling pressure only increases during exercise. Therefore, resting echocardiography alone may miss some patients, requiring a comprehensive assessment of multiple parameters. Furthermore, the results of this examination are greatly affected by the operator's skill level and the quality of the acoustic window, limiting its ability to assess left ventricular diastolic function. Therefore, detecting left ventricular diastolic dysfunction solely through echocardiography is insufficient as a specific diagnostic criterion for HFpEF. Cardiac magnetic resonance imaging (MRI) plays an important role in assessing early cardiac dysfunction, cardiac microcirculatory abnormalities, and tissue characteristics. However, the technology still has limitations such as long scan time, complex image interpretation, high cost, and limited diagnostic specificity.

[0004] Currently, biomarkers for diagnosing heart failure with heart failure (HFpEF) have poor specificity. B-type natriuretic peptide (BNP) and N-terminal pro-BNP (NT-proBNP) are important biomarkers for heart failure diagnosis and have been included in clinical diagnostic guidelines for HFpEF. However, approximately 20% of obese patients with HFpEF have natriuretic peptide levels below the diagnostic threshold. A retrospective analysis published in *Circulation* in 2018 of patients who underwent invasive hemodynamic assessment and were diagnosed with HFpEF found that obesity (body mass index >30 kg / m2), atrial fibrillation, age >60 years, treatment with ≥2 antihypertensive drugs, elevated diastolic filling pressure (E / e') >9, and pulmonary artery systolic pressure >35 mmHg were all significantly associated with the occurrence of HFpEF (P<0.05). Based on the logistic regression association strength between these variables and HFpEF, researchers assigned different scoring weights to six variables to construct the H2FPEF scoring system (Hypertension, HFpEF, Fibrillation, Pulmonary hypertension, Elderly, Filling pressure). Atrial fibrillation was assigned 3 points, obesity 2 points, and the use of ≥2 antihypertensive medications, atrial fibrillation, pulmonary hypertension (PASP > 35 mmHg), age > 60 years, and elevated diastolic filling pressure (E / e' > 9) were each assigned 1 point. The H2FPEF score ranged from 0 to 9 points, with the likelihood of HFpEF increasing with the score. This scoring system effectively distinguishes HFpEF patients from those with non-heart failure-related exertional dyspnea. However, the diagnostic sensitivity of this scoring tool varies significantly across different populations; for example, in the US population, patients with low scores may still have HFpEF. Due to the lack of highly specific biomarkers, the diagnosis of HFpEF remains challenging, thus there is an urgent need to discover new diagnostic and treatment biomarkers to improve diagnostic accuracy and optimize patient management.

[0005] With the continuous development of research techniques and methods such as transcriptomics, metabolomics, proteomics, and lipidomics, our understanding of the metabolic characteristics of heart failure with heart failure (HFpEF) is deepening. In 2023, Professor Kavita Sharma's team further conducted targeted metabolomics analysis on plasma and cardiac tissue of healthy individuals and HFpEF patients. They found that the levels of branched-chain amino acids, fatty acids, and glucose metabolites and their related pathways in the hearts of HFpEF patients were significantly downregulated compared to normal hearts, confirming that changes in myocardial energy metabolism caused by metabolic disorders are an important pathogenesis of HFpEF. The widespread application of metabolomics has identified amino acids as novel biomarkers related to cardiovascular diseases. Studies have shown that plasma amino acid levels can not only be used to differentiate between heart failure with heart failure (HFrEF) and heart failure with heart failure (HFpEF), but also for NYHA risk stratification in patients with heart failure, suggesting that plasma metabolites can serve as effective diagnostic markers for HFpEF. Summary of the Invention

[0006] The purpose of this invention is to solve the problems of complex and poor specificity in the diagnosis of existing HFpEF.

[0007] To achieve the above objectives, the first aspect of the present invention provides the application of a biomarker in the preparation of products for diagnosing or predicting heart failure with preserved ejection fraction, wherein the biomarker is kynurenic acid.

[0008] A second aspect of the present invention provides a kit for detecting a biomarker in a sample and its application in the preparation of products for diagnosing or predicting heart failure with preserved ejection fraction, wherein the biomarker is kynurenic acid.

[0009] A third aspect of the present invention provides a product for diagnosing or predicting heart failure with preserved ejection fraction, the product containing a reagent for detecting a biomarker in a sample, the biomarker being kynurenic acid.

[0010] The fourth aspect of the present invention provides the application of the product described in the third aspect of the present invention in the prediction, diagnosis or prognostic assessment of heart failure with preserved ejection fraction.

[0011] The fifth aspect of the present invention provides a method for establishing a mass spectrometry model for assessing the risk of heart failure with preserved ejection fraction or for use in heart failure with preserved ejection fraction, the method comprising the step of identifying a biomarker in blood samples between patients and healthy controls, wherein the biomarker is kynurenic acid.

[0012] This invention enables the diagnosis of heart failure with preserved ejection fraction by quantitative detection of kynurine in plasma. Compared with existing technologies, this invention provides a non-invasive, rapid, and low-cost diagnostic method; it is applicable to the prediction, diagnosis, or prognostic assessment of heart failure with preserved ejection fraction, achieving early intervention, prevention, or delay in the progression of heart failure with preserved ejection fraction. Attached Figure Description

[0013] Figure 1 This is a graph showing the metabolic disorder phenotype results of the HFpEF model constructed using the "two-hit" method; Figure 2 The HFpEF model constructed using the "two-hit" approach indicates diastolic dysfunction. Figure 3 This is a graph showing the cardiac hypertrophy index results of the HFpEF model constructed using the "two-hit" method. Figure 4 This is a picture of the results of staining mouse heart tissue with wheat germ lectin. Figure 5 This is a diagram showing the results of masson staining of mouse heart tissue. Figure 6 This is a figure showing the analysis results of the significant downregulation of the tryptophan metabolism pathway in the heart tissue of HFpEF mice; Figure 7 The graph shows the effect of decreased plasma canine uric acid levels on the close correlation between reduced HFpEF diastolic function. Figure 8 This is a graph showing the mRNA levels of kynurenate aminotransferase in the heart tissue of HFpEF mice. Figure 9 This is a graph showing the analysis results of the close correlation between decreased plasma kynurenic acid levels and reduced diastolic function in patients with high heart rate pEF. Figure 10 This is a forest plot based on multivariate logistic regression analysis of plasma KYNA levels and the risk of HFpEF. Detailed Implementation

[0014] The endpoints and any values ​​of the ranges disclosed herein are not limited to the precise ranges or values, and these ranges or values ​​should be understood to include values ​​close to these ranges or values. For numerical ranges, the endpoint values ​​of the various ranges, the endpoint values ​​of the various ranges and individual point values, and individual point values ​​can be combined with each other to obtain one or more new numerical ranges, which should be considered as specifically disclosed herein.

[0015] As previously stated, a first aspect of the present invention provides the use of a biomarker in the preparation of products for diagnosing or predicting heart failure with preserved ejection fraction, wherein the biomarker is kynurenic acid.

[0016] Preferably, the level of the biomarker is negatively correlated with the risk of heart failure with preserved ejection fraction.

[0017] Preferably, the application includes assessing the risk of preservative-ejection fraction (PEFF) heart failure in a subject by quantitatively analyzing the levels of biomarkers in the subject's sample. The inventors of this invention first discovered a significant correlation between the aforementioned biomarkers and PEFF by analyzing metabolites in plasma samples; these biomarkers can be used to diagnose or predict PEFF.

[0018] Preferably, the test sample for the product is selected from at least one of serum and plasma.

[0019] Preferably, the product is selected from at least one of a test kit, a test chip, and a test reagent.

[0020] As previously stated, a second aspect of the present invention provides the use of a kit for detecting a biomarker in a sample in the preparation of a product for diagnosing or predicting heart failure with preserved ejection fraction, wherein the biomarker is kynurenic acid.

[0021] Preferably, the kit contains at least one of enzyme-linked immunosorbent assay (ELISA) reagent, colloidal gold reagent, chemiluminescent reagent, flow cytometry quantitative reagent, and mass spectrometry quantitative reagent.

[0022] Preferably, the test sample is selected from at least one of serum and plasma.

[0023] In this invention, the kit can be combined with other methods for detecting preservative ejection fraction (PEF) or PEF diagnostic models to determine whether a person has PEF and the risk of having PEF by detecting the biomarkers in the sample.

[0024] As previously stated, a third aspect of the present invention provides a product for diagnosing or predicting heart failure with preserved ejection fraction, the product containing a reagent for detecting a biomarker in a sample, the biomarker being kynurenic acid.

[0025] Preferably, the product contains at least one of the following: enzyme-linked immunosorbent assay (ELISA) reagent, colloidal gold reagent, chemiluminescent reagent, flow cytometry quantitative reagent, and mass spectrometry quantitative reagent for detecting the level of biomarkers in the sample to be tested.

[0026] In this invention, the diagnosis or prediction is to assess whether the subject has heart failure with preserved ejection fraction.

[0027] In this invention, the product may also include suitable buffer solution (aqueous solution), isotopically labeled or unlabeled peptides, peptide calibration curve standards, developing agents, enzymes, markers, detection methods, control samples, standards, instructions, explanatory information, methods for isolating relevant biomarkers from samples, devices for obtaining samples from individuals (e.g., containers or instruments containing needles), or supports containing pores.

[0028] Preferably, the product is selected from at least one of a test kit, a test chip, and a test reagent.

[0029] As previously stated, the fourth aspect of the present invention provides the application of the product described in the third aspect of the present invention in the prediction, diagnosis or prognostic assessment of heart failure with preserved ejection fraction.

[0030] As previously stated, a fifth aspect of the present invention provides a method for establishing a mass spectrometry model for assessing the risk of heart failure with preserved ejection fraction or for the diagnosis of heart failure with preserved ejection fraction, the method comprising the step of identifying a biomarker in blood samples between patients and healthy controls, wherein the biomarker is kynurenic acid.

[0031] Preferably, the method includes quantitative detection of the biomarker.

[0032] To clarify which metabolites in HFpEF were altered and closely related to diastolic dysfunction, the inventors of this invention first established a mouse model of HFpEF by inducing a 60% high-fat diet (HFD) and Nω-nitro-L-arginine methyl ester hydrochloride (L-NAME). Then, the mice were weighed, and their blood pressure, systolic and diastolic function, adipose tissue content, glucose tolerance, insulin sensitivity, and fatigue exercise time were measured. After these experiments, tissue samples were collected and weighed, and the heart / tibia length was calculated. Mouse heart tissue was stained with hematoxylin / eosin, wheat germ lectin, and masson's stain. Transcriptomic analysis of the mouse hearts was performed to identify the key altered metabolic pathways in the HFpEF model mice. Plasma metabolomics analysis was conducted on the metabolites in the key altered metabolic pathways to clarify the correlation between their levels and diastolic dysfunction. In order to further clarify the clinical significance of kynuric acid in HFpEF, the inventors of this invention tested the plasma kynuric acid level in HFpEF patients and verified the clinical correlation between kynuric acid level and diastolic function indicators.

[0033] This invention is the first to clearly demonstrate that plasma kynurenic acid levels in patients with HFpEF are significantly correlated with diastolic dysfunction in HFpEF, and that kynurenic acid can serve as a biomarker for diagnosing HFpEF.

[0034] The present invention will be described in detail below through examples. Unless otherwise specified, the methods used in the following examples are conventional methods in the art, and the reagents used are all commercially available. Room temperature refers to 25±2℃.

[0035] In the following examples, the Control group represents the control group, and the HFpEF group represents the model group.

[0036] Preparation method of hydrochloric acid alcohol: Mix 198 ml of ethanol (volume fraction of 75%) with 2 ml of hydrochloric acid.

[0037] Ordinary feed: purchased from Beijing Huafukang Biotechnology Co., Ltd., product number 1022.

[0038] High-fat feed: purchased from Research Diet, product number D12492.

[0039] Nitric oxide synthase inhibitor (L-NAME): purchased from Sigma, catalog number N109211.

[0040] Insulin: Purchased from Solarbio, product number I8830.

[0041] Trypsin, purchased from Gibco, catalog number 25200-072.

[0042] Hematoxylin / eosin staining kit: purchased from Solarbio, catalog number G1120.

[0043] Wheat germ agglutinin (WGA) staining solution: purchased from Vector Laboratories, catalog number FL-1021.

[0044] Masson's Trichrome Staining Kit: Purchased from Solarbio, catalog number G1340.

[0045] Trizol reagent: purchased from Invitrogen, catalog number 15596-026-CN.

[0046] VAHTS Universal V5 RNA-seq Library Prep Kit: Purchased from Vazyme, catalog number NR612-02.

[0047] ELISA kit: purchased from Cloud Clone Corp., product number CED718Ge.

[0048] The ready-to-use premixed solution for qPCR first-strand cDNA synthesis (containing 4×Hifair®AdvanceFast SuperMix (NoDye) and 5×gDNA Digester Mix) was purchased from Yisheng Biotechnology (Shanghai) Co., Ltd., model number 11141ES60.

[0049] Example 1: Construction of a "two-hit" HFpEF mouse model Specific pathogen-free (SPF) wild-type male C57BL / 6J mice, 8 weeks old, were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd., and housed in an SPF-grade environment at the Laboratory Animal Department of Capital Medical University. The animal experiments were reviewed and approved by the Ethics Committee of Capital Medical University (Ethics No.: AEEI-2023-165), and the experimental procedures complied with the "Regulations on the Management of Laboratory Animals".

[0050] In C57BL / 6J mice, a 60% high-fat diet (HFD) and the nitric oxide synthase inhibitor L-NAME (0.5 g / L, dissolved in drinking water) successfully mimicked the core characteristics of human HFpEF (diastolic dysfunction combined with metabolic abnormalities). The high-fat diet (60% HFD) induced metabolic abnormalities in the mice, while the nitric oxide synthase inhibitor L-NAME (0.5 g / L, dissolved in drinking water) induced vascular endothelial dysfunction and increased cardiac pressure load.

[0051] The specific method for constructing the "two-hit" HFpEF mouse model is as follows: Eight-week-old male C57 / BL6J mice were randomly divided into a Control group and a HFpEF group, with six mice in each group. The Control group was fed normal diet and normal drinking water daily. The HFpEF group was fed high-fat diet (60% HFD) and drinking water containing 0.5 g / L L-NAME daily.

[0052] This invention successfully replicated the "two-hit" HFpEF model by feeding C57BL / 6J mice with 60% HFD combined with L-NAME. Figure 1 As shown in (A) above. From week 1 of feeding until week 15 of feeding, the mice were weighed every week, and the results are shown below. Figure 1 As shown in (B) of the diagram. Figure 1 In (B), compared with the control group mice, the HFpEF model mice had increased body weight, which indicates that the "two-hit" HFpEF model successfully developed an obesity phenotype under the influence of a high-fat diet.

[0053] Example 2: Detection of adipose tissue content in HFpEF mice After 10 weeks of group feeding, mice were anesthetized with isoflurane via inhalation using a Micro-CT scanner (purchased from Belgium Skyscan Company, model SkyScan1276) for Micro-CT scans. The mice's body temperature was monitored during anesthesia to ensure it remained at 37.0℃. After anesthesia, the mice were placed on the scanning table of the Micro-CT scanner, lying naturally with limbs extended, and secured with tape to prevent positional deviations from affecting the scan results. Micro-CT scans were performed, and image reconstruction was performed using Micro-CT software to obtain three-dimensional reconstructed images of the mice. Analysis was performed using SkyScan1276 Analyze software to extract the volume and density information of the mouse body fat and calculate the body fat content. Results are as follows: Figure 1 As shown in (C). Figure 1 In (C), compared to the Control group mice, the HFpEF group mice had increased body fat. This indicates that the HFpEF model group mice had increased body fat content, which, combined with Example 1, shows that the model mice exhibited metabolic disorders, namely, the first hit: underlying diseases (such as diabetes, obesity) triggering systemic low-grade inflammation.

[0054] Example 3: Non-invasive detection of blood pressure in the tail of HFpEF mice This experiment used a BP-2000 non-invasive blood pressure monitoring system (purchased from Vistach Company, model BP-2000) to measure the systolic blood pressure (SBP) and diastolic blood pressure (DBP) of mice in the Control group and HFpEF group to assess hemodynamic status. The specific detection methods are as follows: 1) Adaptation training: After 8 weeks of group feeding, non-invasive blood pressure measurement was performed on the tail of the mice. Adaptation training was carried out 3 days before the non-invasive blood pressure measurement. The mice were placed in the mouse restraint of the blood pressure monitor for 12 minutes every day to adapt to the environment and tail pressure, reduce stress, and the pressure was not turned on during the training period to avoid the mice having a negative reaction to the device. 2) Formal testing: Gently grasp the mouse and place it in the mouse holder, allowing the tail to extend naturally. After passing through the sensor, secure the tail with tape. During the test, the instrument's heating station is kept at 33°C to promote vasodilation in the tail and improve the success rate of the measurement. 3) Start the device and measure: After marking the mice in order, start the test, pressurize the device to a pressure higher than 200 mmHg, and then slowly release it, recording the blood pressure waveform; 4) Calculation: Measure each mouse 20 times, and take the 10 stable data to calculate the average value; record SBP (systolic blood pressure): the pressure of the tail artery during cardiac systole; and DBP (diastolic blood pressure): the pressure of the tail artery during cardiac diastole.

[0055] SBP test results are as follows Figure 1 As shown in (D), from Figure 1 As shown in (D), compared with the control group mice, the HFpEF group mice had elevated SBP. DBP test results are as follows... Figure 1 As shown in (E), from Figure 1 As shown in (E), compared with the control group mice, the HFpEF group mice had elevated DBP. This indicates that the model group mice had elevated blood pressure, i.e., the second stage: endothelial dysfunction and hypertension.

[0056] Example 4: Testing glucose tolerance in HFpEF mice (Intraperitoneal Glucose Tolerance Test, IPGTT test) This experiment aimed to assess glucose tolerance in HFpEF mice. After 12 weeks of feeding, mice in both the Control and HFpEF groups underwent the IPGTT test. The specific experimental procedures are as follows: 1) Fasting: Fast for 12 hours (usually in the evening, with the experiment conducted the following morning), free access to water; 2) Preparation of glucose solution: Dissolve anhydrous glucose in physiological saline to a concentration of 0.1 g / mL; 3) After fasting, blood was collected from the tail tip, and the basal blood glucose level was measured using a Bayer Glucose Meter (purchased from Contour Plus, model 7600P) (t=0 min), while the weight of the mice was recorded at the same time; 4) Glucose injection: Intraperitoneal injection of 1 g / kg glucose (1 g / kg means injection based on mouse body weight, such as 50 g mouse, glucose concentration 0.1 g / mL, injection 500 μL). 5) Blood glucose monitoring: Blood glucose levels were measured and recorded at different time points (15 min, 30 min, 45 min, 60 min, and 120 min) after glucose injection. 6) Calculation and statistics: Plot the blood glucose change curve, calculate the area under the curve (AUC), and compare the blood glucose rise trend and AUC of different groups.

[0057] The blood glucose level curve results are as follows Figure 1 As shown in (F) in the figure, the AUC results are shown in (G) in the figure. Figure 1 (F) and Figure 1 As can be seen from (G), compared to the control group mice, the HFpEF group mice had impaired glucose tolerance. This indicates that the model group mice exhibited metabolic disorders.

[0058] Example 5: Insulin Tolerance Test (ITT) in HFpEF Mice This experiment aimed to assess the insulin sensitivity of HFpEF mice. After 14 weeks of feeding, ITT (intracytoplasmic insulin resistance) was performed on mice in both the Control and HFpEF groups. The specific experimental procedures are as follows: 1) Fasting: Fasting for 4 hours (generally from 9:00 AM to 1:00 PM), with free access to water; 2) Insulin preparation: Insulin was diluted with physiological saline to a concentration of 0.1 U / L, and fasting blood glucose (t=0 min) and body weight were measured by blood sample taken from the tail tip; 3) Insulin injection: Intraperitoneal injection of 1 U / kg insulin (1 U / kg means injection based on mouse body weight, such as 50 g mouse, insulin concentration 0.1 U / mL, injection 500 μL). 4) Blood glucose monitoring: Blood samples were collected at different time points (15 min, 30 min, 60 min, 90 min) after insulin injection, and blood glucose levels were measured using a Bayer blood glucose meter to record the blood glucose decreasing trend; 5) Calculation and statistics: Plot the blood glucose decrease curve, calculate the AUC (area under the curve), and compare the rate and magnitude of blood glucose decrease among different groups.

[0059] The blood glucose level curve results are as follows Figure 1 As shown in (H) in the figure, the AUC results are shown in (I) in the figure. Figure 1 (H) and Figure 1 As shown in (I), compared to the Control group mice, the HFpEF group mice had impaired insulin sensitivity. This indicates that the model group mice exhibited insulin resistance.

[0060] Examples 1-5 above demonstrate that the model group successfully induced the HFpEF metabolic disorder phenotype.

[0061] Example 6: Detection of cardiac systolic and diastolic function in HFpEF mice In this experiment, cardiac systolic and diastolic function in mice of the Control and HFpEF groups was assessed using echocardiography (using a Vevo 2100 ultra-high resolution small animal color Doppler ultrasound imaging system, purchased from VisualSonics). The specific methods are as follows: 1) After 10 weeks of group feeding, cardiac ultrasound was performed on the mice. The chest hair was removed the day before the ultrasound. 2) Anesthetize mice by isoflurane inhalation to ensure complete anesthesia and no violent shaking during the experiment; use a temperature-controlled pad to ensure that the body temperature of the mice is maintained at 37.0℃ during anesthesia; 3) After anesthesia, the mouse lay flat and was placed on an ultrasound scanning table. An ultrasound probe was used to scan through the left chest. 4) Cardiac imaging using a high-frequency ultrasound probe: ① Two-dimensional mode (B-mode): Displays the structure of the mouse heart, which is then used for global longitudinal strain (GLS) analysis of the myocardium. The GLS% is obtained by tracking the motion trajectory of ultrasound spots within the myocardium using Vevo strain ultrasound analysis software. ② Two-dimensional mode (M-mode): Used to assess the dynamic contraction and relaxation of the heart, providing quantitative analysis of left ventricular diameter and cardiac wall thickness, and calculating ejection fraction (EF) and left ventricular shortening (FS). The formula for calculating EF% is EF% = (LVEDV - LVESV) / ​​LVEDV × 100%; the formula for calculating FS% is FS% = (LVIDd - LVIDs) / LVIDd × 100%. LVEDV refers to the left ventricular end-diastolic volume (LVEDV), LVESV refers to the left ventricular end-systolic volume (LVVESV), LVIDd refers to the left ventricular internal dimension at end-diastole (LVIDd), and LVIDs refers to the left ventricular internal dimension at end-systole (LVIDs). end-Systole, LVIDs); ③ Pulse Doppler: Used to assess blood flow velocity and diastolic blood flow velocity, and to assess diastolic function of the mouse heart; ④ Tissue Doppler: Imaging was performed at the base of the left ventricle, with particular attention to the motion signals of myocardial tissue around the mitral valve annulus, to assess diastolic function in mice. ⑤ Early diastolic flow (E wave): The rapid filling peak of the left ventricle in the early diastolic phase, reflecting the speed at which blood enters the left ventricle and the heart's ability to diastole. ⑥ Late diastolic flow (A wave): The filling peak during late diastole (atrial contraction), caused by atrial contraction, reflects the diastolic function of the heart, and is used to calculate E / A. ⑦ Early diastolic wave (e' wave): The velocity of myocardial motion in the early diastolic phase at the mitral valve annulus, reflecting the heart's diastolic capacity, and is used to calculate E / e'. ⑧ The Tei index, also known as the myocardial comprehensive index, is the ratio of the sum of the isovolumetric contraction time (IVCT) and the isovolumetric relaxation time (IVRT) to the ejection time (ET), i.e., Tei index = (IVCT + IVRT) / ET; Typical ultrasound images Figure 2 As shown in (A).

[0062] EF% test results are as follows Figure 2 As shown in (B). Figure 2 In (B), the EF% of the control group and the HFpEF model group were the same, indicating that both groups retained ejection fraction.

[0063] E / e' test results are as follows Figure 2 As shown in (C). Figure 2 In (C), the E / e' ratio was higher in the model group compared to the control group.

[0064] E / A test results are as follows Figure 2 As shown in (D). Figure 2 In (D), the E / A ratio was higher in the model group compared to the control group.

[0065] GLS% test results are as follows: Figure 2 As shown in (E). Figure 2 In (E), the GLS% ratio was higher in the model group compared to the control group.

[0066] The Tei index test results are as follows: Figure 2 As shown in (F). In Figure 2 In (F), the Tei index ratio of the model group was higher than that of the control group.

[0067] In summary, while EF% remained unchanged, the E / e' and E / A ratios increased, and GLS% and the Tei index (myocardial work index) all significantly increased, indicating that the model group experienced diastolic dysfunction.

[0068] Example 7: Detection of fatigue exercise time in HFpEF mice This experiment used a rotarod fatigue tester (purchased from Beijing Zhongshi Technology, product number Zs-rdm-xs) to assess the exercise fatigue time of mice, reflecting their exercise tolerance time. The specific experimental steps are as follows: 1) Adaptation training: After 11 weeks of group feeding, mice were subjected to rotarod fatigue test. 30 minutes before the adaptation training and the formal experiment, the mice were placed in the laboratory to adapt to the environment and reduce stress response. 2) Adaptation training (once a day for 3 days) Parameter settings: Set a fixed speed of 10 rpm for 10 minutes to allow the mouse to adapt to the rotation of the rolling bar. Gradually increase the speed: Day 1: 10 rpm, Day 2: 15 rpm, Day 3: 20 rpm; 3) Formal experiment: On the fourth day, the formal experiment was conducted. The mice were allowed to adapt to the experimental environment for 30 minutes. The status of the rod rotator was checked to ensure that the rotation speed was uniform and the time was recorded accurately. 4) Parameter settings: Place the mouse on the rotating bar and conduct the experiment at 20 rpm (fixed speed). Record the time the mouse stays on the bar (unit: seconds). The experiment is terminated when the mouse falls off the rotating bar or the mouse grabs the rotating bar with its paws three times in a row and is passively rotated more than 2 times. The maximum observation time is 10 minutes. 5) Analysis and Statistics: Each mouse underwent three experiments (15 min apart), and the average value was taken as the final result. The fatigue time of the mice was measured using a rotarod fatigue meter, and the results are as follows: Figure 2 As shown in (G). In Figure 2 In (G), compared with the control group mice, the HFpEF model group mice had a significantly shorter stick time. This indicates that the model group mice exhibited significant exercise intolerance.

[0069] Example 8: Measurement of heart weight and tibia length in HFpEF mice At 15 weeks of group feeding, tissue samples were collected. Surface fluid was aspirated from the perfused hearts, and the wet weight (HW, mg) was measured. Simultaneously, tibia length (TL, mm) was measured using calipers. Cardiac hypertrophy was assessed by calculating the heart weight / tibia length ratio. Results are shown below. Figure 3 (A) In Figure 3 As can be seen in (A), the ratio of heart weight to tibial length increased in the model group, indicating that the heart in the model group was hypertrophic.

[0070] Example 9: Observation of Cardiac Pathology (Hematoxylin / Eosin Staining) In this experiment, mice in the Control and HFpEF groups were euthanized after 15 weeks of feeding. The upper third of the heart was then harvested for paraffin sectioning. Hematoxylin-eosin (H&E) staining of the paraffin sections was performed to further determine whether the heart was enlarged and whether inflammatory cells were infiltrated. The experimental procedures are as follows: 1) Fixation: Use paraffin sections of heart samples and bake them in an oven at 50°C for 1 hour; 2) Dewaxing: Immerse the sections in xylene twice for 10 min each time to achieve dewaxing; then immerse the dewaxed sections in 100% (v / v) ethanol (EtOH) twice for 5 min each time; then immerse the sections in 95% (v / v) EtOH and 85% (v / v) EtOH respectively for 5 min each; wash with distilled water for 2 min; immerse the sections in hematoxylin staining solution for 10 min (basic staining agent, staining cell nuclei), and rinse with running water for 1 min; 3) Staining: Rinse the sections with hydrochloric acid alcohol for 4 seconds, then rinse with running water for at least 30 minutes; then soak the sections in distilled water for 1 minute; then soak the sections in 80% (v / v) EtOH for 5 minutes; then stain the sections with eosin for 15 seconds. 4) Dehydration: Immerse the slices in 95% (v / v) EtOH for 5 min; then immerse the slices in 100% (v / v) EtOH twice, 5 min each time; then immerse the slices in xylene twice, 5 min each time. 5) Mounting: Mount with neutral resin.

[0071] 6) Images were observed and acquired using a GT450 fully automated slide scanning system (purchased from Leica, model: GT450). The results of hematoxylin / eosin staining of mouse hearts are shown below. Figure 3 As shown in (B) of the diagram. From Figure 3 As can be seen in (B), compared with the Control group mice, the HFpEF group mice had an increased heart weight / tibia length ratio and a larger gross cross-sectional area of ​​the heart, indicating that the heart in the model group was thicker and that cardiac remodeling occurred in the model group.

[0072] Example 10: Observation of Cardiac Pathology (Wheat Germ Agglutinin Staining) Staining paraffin sections of heart samples with wheat germ agglutinin (WGA) allows for the detection of cardiomyocyte membranes and extracellular matrix, reflecting the cross-sectional area of ​​cardiomyocytes. The steps of the WGA staining protocol are as follows: 1) Dewaxing of tissue sections: The paraffin sections of the heart sample were immersed in xylene twice for 10 min each time to achieve dewaxing; then the dewaxed sections were immersed in 100% (v / v) EtOH twice for 5 min each time; then the sections were placed in 95% (v / v) EtOH and 85% (v / v) EtOH respectively for 5 min each time; and washed 3 times with PBS (phosphate buffer) for 5 min each time. 2) Antigen retrieval: Add 40 μL of 0.05% trypsin to the slide and incubate at 37°C for 30 min; 3) Blocking and permeabilization: Add 40 μL of a mixture of 1% (w / v) BSA (bovine serum albumin) and 0.2% (v / v) Triton X-100 to the slide, incubate at room temperature for 30 min, and then wash three times with PBS for 5 min each time. 4) WGA staining: Add 40 μL of WGA staining solution (prepared by adding 1 μL of WGA to 500 μL of PBS) to the slide and incubate at room temperature for 2 h; 5) Wash with PBS 3 times, 5 min each time. Add 20 μL of DAPI-containing anti-fluorescence quenching mounting medium (purchased from Solarbio, catalog number S2110) to the slide and gently cover with a coverslip from one side. 6) The sections were observed and images were acquired using a fluorescence microscope (Leica DM6000B). Under a 594 nm laser, the myocardial cell membrane stained with WGA emitted red fluorescence. The cross-sectional area of ​​the myocardium was quantified using ImageJ software. A typical image of wheat germ lectin staining results in mouse heart tissue is shown below. Figure 4 As shown in (A), the statistical results of the heart cross-sectional area are as follows: Figure 4 As shown in (B) of the diagram. From Figure 4 As can be seen from (A) and (B) in the figure, the cross-sectional area of ​​cardiomyocytes in the HFpEF group mice is larger than that in the Control group mice, which further illustrates that cardiac remodeling occurs in the HFpEF group.

[0073] Example 11: Observation of Cardiac Pathology (Marson Trichrome Staining) The degree of cardiac fibrosis can be determined by performing Masson's trichrome staining on paraffin sections of heart samples. The steps for Masson's trichrome staining are as follows: 1) Fixation: Use paraffin sections of heart samples and bake in a 50°C oven for 1 hour; 2) Dewaxing: The paraffin sections of the heart sample were immersed in xylene twice for 10 min each time to achieve dewaxing; then the dewaxed sections were immersed in 100% (v / v) EtOH twice for 5 min each time; then the sections were immersed in 95% (v / v) EtOH and 85% (v / v) EtOH for 5 min each time; washed with distilled water for 2 min; and then soaked in Masson A solution at room temperature overnight. 3) Staining 1: Place the sections soaked in Masson A solution in a 65°C oven for 30 min; at the same time, preheat the sections in Masson D solution and Masson F solution in a 65°C oven for 30 min; wash the oven-baked sections with distilled water for 30 sec. 4) Staining 2: Mix 1 mL of Masson B solution with an equal volume of Masson C solution, add the mixture to the slide and stain for 1 min, then wash with distilled water for 30 sec. 5) Staining 3: Place the sections in 1% hydrochloric acid alcohol for 1 min, rinse with distilled water for 30 sec, and then stain the sections with Masson D solution for 5 min, Masson E solution for 1 min, and Masson F solution for 30 sec respectively. 6) Decolorization: Immerse the slices in 1% (v / v) glacial acetic acid three times, 8 seconds each time, and then immerse the slices in 100% (v / v) EtOH three times, for 5 seconds, 10 seconds and 30 seconds respectively. 7) Dehydration: The slices were immersed in n-butanol twice, for 30 seconds and 2 minutes respectively, and then immersed in xylene twice, for 5 minutes each time; 8) Mounting: Mount with neutral resin.

[0074] Images were acquired using a GT450 fully automated slide scanning system (purchased from Leica, model: GT450). The results of Masson's trichrome staining of mouse heart tissue are as follows: Figure 5 As shown in (A) above. The statistical results of fibrosis are as follows: Figure 5 As shown in (B) of the diagram. From Figure 5 As shown in (A) and (B), compared to the control group mice, the HFpEF group mice had increased cardiac fibrosis. This indicates that the HFpEF group experienced adverse cardiac remodeling.

[0075] Example 12: Transcriptomic analysis of the heart in HFpEF mice To reveal the metabolic characteristics of HFpEF, this invention performed RNA sequencing (RNA-Seq) on the heart tissues of mice in the Control group and the HFpEF model group. The experimental methods are as follows: At 15 weeks of feeding, mice in the Control and HFpEF groups were anesthetized, and blood was collected from the carotid artery. After thoracotomy, the heart was perfused with physiological saline from the apex and then separated to obtain the mouse heart. Total RNA was extracted from the heart using Trizol reagent, and the extraction steps are as follows: 1) Take 10 mg of heart tissue and add 1 mL of Trizol. Homogenize the tissue using a tissue homogenizer (purchased from Servicebio, model SWE-FP) and incubate at room temperature for 5 min to allow the RNA to dissolve completely. 2) Add 200 µL of chloroform, invert for 15 seconds, let stand at room temperature for 5 min, and repeat the inversion process 3 times after the layers separate; centrifuge at 13500 rpm and 4℃ for 15 min, and take about 450 μL of the upper aqueous phase. 3) Add an equal volume of isopropanol, mix gently, and incubate overnight at -20°C; 4) Centrifuge at 13500 g for 10 min at 4°C, discard the supernatant, and retain the RNA precipitate; 5) Add 1 mL of ethanol (75% by volume), gently tap the precipitate, centrifuge at 4°C for 10 min under a centrifugal force of 13500 g, discard the ethanol, and repeat this step twice. 6) Air dry at room temperature; 7) Add 25 µL of RNase-free water, dissolve at 4℃ for 10 min, and determine the concentration and purity (ideal value of 260 / 280 is 2.0) using a NanoDrop 2000 spectrophotometer (purchased from Thermo). 8) Store at -80°C or use immediately for RNA sequencing or reverse transcription.

[0076] After total RNA extraction, the RNA was sent to Shanghai Ouyi Biomedical Technology Co., Ltd. for RNA sequencing. RNA integrity was assessed using an Agilent 2100 Bioanalyzer (purchased from Agilent Technologies, model 2100) before sequencing. Transcriptome libraries were constructed using the VAHTSUniversal V5 RNA-seq Library Prep kit according to the manufacturer's instructions. The libraries were sequenced using a Llumina Novaseq 6000 sequencing platform (purchased from Llumina Technologies, model Novaseq 6000), generating 150 bp paired-end reads; approximately 52-54 raw reads were obtained per sample. Low-quality sequences were filtered: the FASTQ format raw reads were processed using FASTP software to remove low-quality reads, resulting in clean reads for subsequent data analysis. Alignment to a reference genome was performed using HISAT2 software, and gene expression levels (FPKM) were calculated. Read counts for each gene were obtained using HTSeq-count. Differentially expressed gene analysis: PCA analysis and plotting of gene counts were performed using R (v 3.2.0) to assess sample biological repeatability. Differentially expressed gene analysis was conducted using DESeq2 software, where genes meeting the thresholds of q-value < 0.05 and foldchange > 2 or foldchange < 0.5 were defined as differentially expressed genes (DEGs). KEGG Pathway enrichment analysis was performed on differentially expressed genes based on the hypergeometric distribution algorithm to screen for significant enrichment functional items. Enrichment analysis circles were plotted using R (v 3.2.0) for significant enrichment functional items. Gene set enrichment analysis was performed using GSEA software. Using a predefined gene set, genes were ranked according to their differential expression levels in the two sample classes, and then it was examined whether the predefined gene set was enriched at the top or bottom of the ranking list.

[0077] PCA analysis was performed on mice in the Control group and the HFpEF model group, and the results are as follows: Figure 6 As shown in (A) in the diagram. Figure 6 In (A), the heart tissues of mice in the Control group and the HFpEF model group came from different sample clusters. Analysis of differentially expressed genes between the Control and HFpEF model mice yielded the following results: Figure 6 As shown in (B) of the diagram. Figure 6 In (B), compared with the Control group, the HFpEF model group had 344 significantly increased differentially expressed genes and 473 significantly decreased differentially expressed genes (|log2FC|≥1, P≤0.05).

[0078] Further KEGG pathway enrichment analysis was performed on the 473 genes that showed significant reductions, and the results are as follows: Figure 6 As shown in (C) in the diagram. Figure 6 (C) shows that the amino acid metabolism pathway is the most significantly reduced among metabolism-related pathways.

[0079] KEGG pathway subclass enrichment was performed on amino acid metabolic pathways, and the results are as follows: Figure 6 As shown in (D). Figure 6 In (D), compared with the Cotrol group, the tryptophan metabolism pathway in the model group was significantly reduced (P=0.00037).

[0080] Enrichment analysis of the GSEA gene set for amino acid metabolic pathways yielded the following results: Figure 6 As shown in (E). Figure 6 In (E), compared with the Cotrol group, the tryptophan metabolism pathway in the model group was significantly downregulated (ES=-0.490, P=0.002). These results indicate that the tryptophan metabolism pathway in the cardiac tissue of HFpEF mice is significantly reduced.

[0081] Example 13: Targeted metabolomics detection in mouse plasma To further clarify the changes in the levels of different metabolites in the tryptophan pathway in HFpEF model mice, this invention performed targeted tryptophan pathway metabolomics detection on plasma from Control and HFpEF model mice. This experiment used targeted metabolomics technology (LC-MS / MS) to quantitatively analyze tryptophan (Trp) and its metabolites (such as kynurenine and kynurenic acid) in the plasma of Control and HFpEF group mice, respectively. The experimental procedure included: 1) Collection of plasma samples from mice in the Control group and HFpEF group In this experiment, after feeding mice in the Control group and HFpEF group for 15 weeks, the mice in both groups were anesthetized with an overdose of tribromoethanol and blood was collected from their carotid arteries to obtain plasma.

[0082] 2) Sample processing and data analysis: Blood samples were centrifuged at 4℃ and 3000 rpm for 10 min to separate plasma, which was then sent to Shanghai Zhongke New Life Biotechnology Co., Ltd. for targeted metabolomics detection. The association between metabolites and the disease was assessed by differential analysis (T-test, VIP value) of tryptophan downstream metabolite levels between the Control group and the model group, KEGG pathway enrichment (e.g., map00380), and ROC curve analysis. Data were then normalized.

[0083] Results of the metabolomics heatmap targeting tryptophan metabolites are as follows Figure 7 (A). The results of plasma kynurenine and kynurenic acid content measurements in the Control group and model group mice are shown below. Figure 7 As shown in (B) of the diagram. Figure 7 As shown in (B), compared with the Control group, the levels of tryptophan metabolic pathway metabolites such as kynurenine (P=0.0034) and kynurenic acid (P=0.0060) were significantly reduced in the model group. Pearson correlation analysis was performed on the levels of downstream tryptophan metabolites and diastolic dysfunction indicators, and the results are as follows... Figure 7 (C) in the middle. Figure 7 (C) in the figure shows that kynurenic acid is most strongly associated with diastolic function.

[0084] Example 14: Detection of kynuric acid levels in mouse plasma The levels of kynuric acid in the plasma of mice in the Control and HFpEF groups were detected using an ELISA kit (catalog number CED718Ge). The specific experimental steps are as follows: (1) Collection of plasma samples from mice in the Control group and HFpEF group In this experiment, after feeding mice in the Control group and HFpEF group for 15 weeks, the mice in both groups were anesthetized with an overdose of tribromoethanol and blood was collected from their carotid arteries to obtain plasma.

[0085] (2) Detection of plasma canine uric acid levels 1) Rewarming: Warm the reagents in the kit to room temperature in advance; prepare the standard, test reagent A, test reagent B and washing solution according to the instructions of the ELISA kit.

[0086] 2) Sample addition 1: Add 50 μL of standard or sample to each well, followed immediately by 50 μL of test reagent A; shake well to mix; incubate at 37°C for 1 hour; aspirate the liquid from the wells, add 350 μL of washing solution to each well and wash, repeating the washing step 3 times; 3) Sample addition 2: Add 100 μL of test reagent B and incubate at 37°C for 30 minutes; for the liquid in the wells, add 350 μL of washing solution to each well and wash, repeating the washing step 5 times; 4) Sample addition 3: Add 90 μL of substrate solution and incubate at 37°C for 10-20 minutes; 5) Sample addition 4: Add 50 μL of stop solution, gently shake to mix, and immediately use an ELISA reader (purchased from Meigu Molecular Instruments Co., Ltd., model SpectraMax i3x) to detect the optical density at a wavelength of 450 nm. Calculation: Plot the standard curve according to the instructions. Calculate the actual concentration of the sample based on the standard curve and OD values.

[0087] The results of the detection of kynuric acid levels in the plasma of Control group mice and HFpEF model mice are shown in the figure. Figure 7 As shown in (D). Figure 7 In (D), compared with the Control group, the plasma kynuric acid level in the HFpEF model group was decreased, a result consistent with the targeted metabolism results. Both indicate a decrease in plasma kynuric acid level in the HFpEF model.

[0088] Example 15: Analysis of kynurenate aminotransferase mRNA levels in heart tissue of HFpEF mice Using the GAPDH gene as an internal reference, the mRNA levels of Kyat1 and Kyat3, key enzymes in the tryptophan metabolism pathway, were validated. The specific steps are as follows: 1) RNA extraction, the steps are the same as in Example 12; 2) Reverse transcription ① gDNA removal: Prepare the gDNA removal system in an RNase-free centrifuge tube, gently mix with a pipette, and incubate at 42°C for 2 min. The gDNA removal system is shown in Table 1: Table 1:

[0089] ②cDNA Synthesis: Using the reaction solution from the previous step, add 4×Hifair®AdvanceFastSuperMix (No Dye) according to the following system. The reverse transcription reaction system is prepared as shown in Table 2: Table 2:

[0090] Reverse transcription reaction conditions: reverse transcription at 55℃ for 15 min, terminate the reaction at 85℃ for 5 min; incubate at 4℃ for 10 min; dilute at a ratio of 1:3 (i.e., 20 μL of cDNA stock solution added to 40 μL of DEPC water) before subsequent experiments or store at -20℃. 3) qPCR detection: cDNA, 2×NovoStart® SYBR qPCR SuperMix Plus, target gene primers, and RNase-free water were prepared according to Table 3, and amplification was performed according to Table 4. Two replicates were used for each sample to reduce experimental error. The reaction volume (10 µL) is shown in Table 3, the qPCR reaction conditions are shown in Table 4, and the primers used are shown in Table 5. 2×NovoStart® SYBR qPCR SuperMix Plus was purchased from Suzhou Nearshore Protein Technology Co., Ltd., catalog number E096-01B.

[0091] Table 3:

[0092] Table 4:

[0093] Table 5: Primer information for Kyat1, Kyat3, and GAPDH

[0094] 4) qPCR data analysis Gene expression levels were compared between the Cotrol group and the model group using the ΔΔCt calculation method. The mRNA levels of Kyat1, a key enzyme in the tryptophan metabolism pathway, were validated; results are shown below. Figure 8 (A). In Figure 8 In (A), compared to the control group, the mRNA level of Kyat1, a key enzyme in the metabolism of kynurenine to kynurenic acid, was significantly reduced in the HFpEF model group (P=0.0043). The mRNA level of Kyat3, a key enzyme in the tryptophan metabolism pathway, was validated, and the results are shown in [Figure 1]. Figure 8 (B) in Figure 8In (B), compared to the control group, the mRNA level of Kyat3, a key enzyme in the metabolism of kynurenine to kynurenic acid, was significantly reduced in the HFpEF model group (P=0.0165). This suggests that the reduced Kyat enzyme level may be a possible reason for the decreased kynurenic acid level in the model.

[0095] Example 16: Detection of uric acid levels in kyphotic HFpEF patients and healthy individuals To clarify the clinical correlation between kynurenic acid levels and diastolic function, this invention collected plasma and patient information from HFpEF patients and healthy controls, and used ELISA to detect plasma kynurenic acid levels in HFpEF patients and healthy controls, respectively.

[0096] (1) Inclusion criteria This study employed a case-control design, with the case group consisting of patients with heart failure with pulmonary embolism (HFpEF) and the control group consisting of healthy controls with no significant abnormalities in cardiac structure and function. All participants were from Beijing Anzhen Hospital, Capital Medical University. Based on the current diagnostic criteria for HFpEF, participants meeting the inclusion and exclusion criteria, along with control participants from a concurrent health checkup center meeting the inclusion and exclusion criteria, were selected into the cohort. Blood biosamples were collected, and clinical biochemical and ultrasound indicators were analyzed to explore the correlation between plasma metabolite levels and diastolic function.

[0097] 1.1 HFpEF patients Inclusion criteria: 1) The inclusion criteria for HFpEF refer to the diagnostic criteria for HFpEF in the "Chinese Expert Consensus on the Diagnosis and Treatment of Heart Failure with Preserved Ejection Fraction 2023": ① Symptoms and / or signs of heart failure (shortness of breath, persistent cough, dyspnea, fatigue, abdominal distension, loss of appetite, nausea, lung rales, edema (lower extremities, ankles) or ascites); ②The most recent assessment prior to enrollment showed an LVEF ≥ 50%; ③ Elevated N-terminal pro-brain natriuretic peptide (NT-proBNP) levels (i.e., >125 pg / mL in patients without atrial fibrillation or >365 pg / mL in patients with atrial fibrillation); ④ Evidence of cardiac structural changes on echocardiography (such as left atrial enlargement and / or left ventricular hypertrophy, E / e'≥15); ⑤ If neither ③ nor ④ can be met, then stress echocardiography or invasive hemodynamic testing is required. A positive result can also be diagnosed if one of the following criteria is met: A. Stress echocardiography showed an E / e' ≥ 15; B. Cardiac catheterization at rest, PCWP ≥ 15 mmHg or LVEDP ≥ 16 mmHg; C. Stress catheterization, peak PCWP ≥ 25 mmHg during exercise. ⑥ Based on the H2FPEF score, scores greater than 5 will be considered for inclusion. The scoring details are shown in Table 6: Table 6:

[0098] 2) Vital signs are stable; clinical data is complete, echocardiography is available, and the patient agrees to provide a blood sample; 3) Obtain informed consent, voluntarily participate in this project, and sign an informed consent form.

[0099] Exclusion criteria: 1) Patients with severe organ dysfunction and cardiac abnormalities, such as: a history of congenital heart disease, known infiltrative cardiomyopathy, myocarditis or hypertrophic cardiomyopathy, acute myocardial infarction, unstable angina, cardiovascular and cerebrovascular accidents, valvular heart disease, pericardial constriction and severe coronary artery disease, severe arrhythmia or bradycardia; severe liver and kidney dysfunction; 2) Patients with heart failure caused by other factors such as immune system or blood system diseases; 3) Pregnant or breastfeeding women; 4) Patients admitted with severe trauma, infection, or recent surgery; 5) Individuals with mental illness or cognitive impairment who are unable to cooperate with this study.

[0100] 1.2 Healthy Control Group Inclusion criteria: 1) Healthy controls whose heart and blood flow showed no obvious abnormalities upon ultrasound examination; 2) Healthy individuals from the Beijing Anzhen Hospital Health Checkup Center whose age, gender, and BMI were matched to those of the case group; 3) Obtain informed consent, voluntarily participate in this project, and sign an informed consent form.

[0101] Exclusion criteria: 1) History of serious illness; 2) No informed consent form was signed.

[0102] (2) Plasma sample collection All study subjects (40 patients with HFpEF and 40 healthy individuals) fasted for 10 hours. Five ml of venous blood was drawn from each sample using sterile EDTA anticoagulant blood collection tubes by medical staff at Beijing Anzhen Hospital, Capital Medical University. All procedures followed clinical blood collection protocols and standards. The collected blood samples were centrifuged at 4°C and 3000 rpm for 10 minutes, and plasma was collected. The aliquoted plasma samples were stored at -80°C for later use.

[0103] (3) The detection of plasma canine uric acid level is the same as in Example 14.

[0104] The results of canine uric acid level testing are as follows Figure 9 As shown in (A) in the diagram. Figure 9 In (A), compared with the control group, the plasma kynuric acid level in HFpEF patients (case group) was significantly lower.

[0105] To further clarify the correlation between kynurenic acid levels and diastolic function, this invention performs a correlation analysis between E / e' and kynurenic acid levels, and the results are as follows: Figure 9 As shown in (B) of the diagram. Figure 9 In (B), E / e' shows a significant negative correlation with kynurenic acid. This invention performs a correlation analysis between BNP and kynurenic acid levels, and the results are as follows... Figure 9 As shown in (C). Figure 9 In (C), BNP showed a significant negative correlation with kynurenic acid. The results of logistic regression analysis of kynurenic acid levels and the presence of HFpEF are as follows: Figure 10 As shown. Figure 10 The results showed that kynuric acid levels were significantly negatively correlated with HFpEF, and were not related to sex, age, atrial fibrillation, hypertension, or diabetes (fourth quartile; unadjusted odds ratio [OR], 0.078 [95% CI, 0.014–0.437], P = 0.004; adjusted odds ratio [Model 1], 0.098 [95% CI, 0.012–0.801], P = 0.03). When TC and HLD were included in the multivariate logistic regression model (adjusted Model 2), the negative correlation between kynuric acid levels and HFpEF was weakened (adjusted OR [Model 2], 0.099 [95% CI, 0.01–0.999], P = 0.05). These results indicate that clinically, HFpEF kynuric acid levels are significantly decreased, and the decrease in kynuric acid levels is closely related to reduced diastolic function.

[0106] The preferred embodiments of the present invention have been described in detail above; however, the present invention is not limited thereto. Within the scope of the inventive concept, various simple modifications can be made to the technical solutions of the present invention, including combinations of various technical features in any other suitable manner. These simple modifications and combinations should also be considered as the content disclosed in the present invention and are all within the protection scope of the present invention.

Claims

1. The application of biomarkers in the preparation of products for diagnosing or predicting heart failure with preserved ejection fraction, characterized in that, The biomarker is kynurenic acid.

2. The application according to claim 1, characterized in that, The levels of the biomarkers were negatively correlated with the risk of heart failure with preserved ejection fraction.

3. The application according to claim 1, characterized in that, The applications include assessing the risk of preservative ejection fraction heart failure in subjects by quantitatively analyzing the levels of biomarkers in subject samples.

4. The application according to claim 1, characterized in that, The test samples for the product are selected from at least one of serum and plasma. The product is selected from at least one of the following: test kit, test chip, and test reagent.

5. The application of a kit for detecting biomarkers in a sample in the preparation of products for diagnosing or predicting heart failure with preserved ejection fraction, characterized in that, The biomarker is kynurenic acid.

6. The application according to claim 5, characterized in that, The kit contains at least one of the following: enzyme-linked immunosorbent assay (ELISA) reagent, colloidal gold reagent, chemiluminescent reagent, flow cytometry quantitative reagent, and mass spectrometry quantitative reagent.

7. A product for diagnosing or predicting heart failure with preserved ejection fraction, characterized in that, The product contains a reagent for detecting biomarkers in samples, and the biomarker is kynurenic acid.

8. The product according to claim 7, characterized in that, The product contains at least one of the following: enzyme-linked immunosorbent assay (ELISA) reagent, colloidal gold reagent, chemiluminescence reagent, flow cytometry quantitative reagent, and mass spectrometry quantitative reagent for detecting the level of biomarkers in the sample to be tested.

9. The use of the product of claim 7 or 8 in predicting, diagnosing, or assessing the prognosis of heart failure with preserved ejection fraction.

10. A method for establishing a mass spectrometry model to assess the risk of heart failure with preserved ejection fraction or for use in heart failure with preserved ejection fraction, characterized in that, The method includes the step of identifying a biomarker in blood samples from patients and healthy controls, wherein the biomarker is kynurenic acid; Preferably, the method includes quantitative detection of the biomarker.

Images