A biomarker for diagnosing right heart failure after surgery of hypertrophic obstructive cardiomyopathy and application thereof

Abstract

The application provides a biomarker for diagnosing right heart failure after hypertrophic obstructive cardiomyopathy surgery and application thereof, and belongs to the technical field of biomarkers. A metabolic marker comprises at least two metabolites: methylguanidine, D-glucuronide, 3-hydroxyoctanoic acid, phosphatidylcholine and 3-hydroxytetradecenoyl carnitine. The application constructs a metabolite diagnostic model based on the metabolic marker or in combination with serum creatinine and pulmonary arterial hypertension as biomarkers, which shows excellent diagnostic performance. The biomarker provided by the application is used for diagnosing the risk of right heart failure after hypertrophic obstructive cardiomyopathy surgery and development of a diagnostic system thereof.

Description

Technical Field

[0001] This application belongs to the field of biomarker technology, specifically relating to a biomarker for diagnosing right heart failure after surgery for hypertrophic obstructive cardiomyopathy and its application. Background Technology

[0002] Hypertrophic obstructive cardiomyopathy (HOCM) is a severe form of ventricular septal hypertrophy that leads to left ventricular outflow tract obstruction (LVOT). For patients with symptomatic obstruction, surgical ventricular septal myocardectomy remains an effective and widely accepted treatment strategy. Pulmonary hypertension (PH) is a common and clinically significant complication of HOCM. Studies have shown that PH gradually increases right ventricular afterload, eventually leading to right heart failure (RHF) and even death.

[0003] Preoperative identification and assessment of the probability of postoperative right ventricular failure in patients with hypertrophic obstructive cardiomyopathy are crucial for personalized treatment. However, there are currently no reports on biomarkers for diagnosing postoperative right ventricular failure in patients with hypertrophic obstructive cardiomyopathy. Summary of the Invention

[0004] The purpose of this invention is to provide a biomarker for diagnosing right heart failure (HOCM-RHF) after surgery for hypertrophic obstructive cardiomyopathy. By using a specific combination of screened metabolites, HOCM-RHF can be diagnosed rapidly and accurately, avoiding invasive damage and objectively and accurately reflecting patient outcomes.

[0005] The present invention provides a metabolic marker comprising at least two of the following metabolites: methylguanidine, D-glucuronic acid, 3-hydroxyoctanoic acid, phosphatidylcholine, and 3-hydroxytetradecenoic carnitine.

[0006] Preferably, it includes at least one of the following: a first biomarker composed of methylguanidine and D-glucuronic acid; a second biomarker composed of methylguanidine and 3-hydroxyoctanoic acid; a third biomarker composed of methylguanidine and phosphatidylcholine; a fourth biomarker composed of methylguanidine and 3-hydroxytetradecanoic acid; a fifth biomarker composed of D-glucuronic acid and 3-hydroxyoctanoic acid; a sixth biomarker composed of D-glucuronic acid and phosphatidylcholine; a seventh biomarker composed of D-glucuronic acid and 3-hydroxytetradecanoic acid; an eighth biomarker composed of 3-hydroxyoctanoic acid and phosphatidylcholine; a ninth biomarker composed of 3-hydroxyoctanoic acid and 3-hydroxytetradecanoic acid; and a tenth biomarker composed of phosphatidylcholine and 3-hydroxytetradecanoic acid.

[0007] Preferably, it includes at least one of the following: an eleventh biomarker composed of methylguanidine, D-glucuronic acid, and 3-hydroxyoctanoic acid; a twelfth biomarker composed of methylguanidine, D-glucuronic acid, and phosphatidylcholine; a thirteenth biomarker composed of methylguanidine, D-glucuronic acid, and 3-hydroxytetradecanoic acid; a fourteenth biomarker composed of methylguanidine, 3-hydroxyoctanoic acid, and phosphatidylcholine; a fifteenth biomarker composed of methylguanidine, 3-hydroxyoctanoic acid, and 3-hydroxytetradecanoic acid; and a biomarker composed of methylguanidine, D-glucuronic acid, and 3-hydroxytetradecanoic acid. The sixteenth biomarker consists of guanidine, phosphatidylcholine, and 3-hydroxytetradecanoic acid; the seventeenth biomarker consists of D-glucuronic acid, 3-hydroxyoctanoic acid, and phosphatidylcholine; the eighteenth biomarker consists of D-glucuronic acid, 3-hydroxyoctanoic acid, and 3-hydroxytetradecanoic acid; the nineteenth biomarker consists of D-glucuronic acid, phosphatidylcholine, and 3-hydroxytetradecanoic acid; and the twentieth biomarker consists of 3-hydroxyoctanoic acid, phosphatidylcholine, and 3-hydroxytetradecanoic acid.

[0008] Preferably, it includes at least one of the following: a 21st biomarker composed of methylguanidine, D-glucuronic acid, 3-hydroxyoctanoic acid and phosphatidylcholine; a 22nd biomarker composed of methylguanidine, D-glucuronic acid, 3-hydroxyoctanoic acid and 3-hydroxytetradecenoic acid; a 23rd biomarker composed of methylguanidine, 3-hydroxyoctanoic acid, phosphatidylcholine and 3-hydroxytetradecenoic acid; a 24th biomarker composed of methylguanidine, D-glucuronic acid, phosphatidylcholine and 3-hydroxytetradecenoic acid; and a 25th biomarker composed of D-glucuronic acid, 3-hydroxyoctanoic acid, phosphatidylcholine and 3-hydroxytetradecenoic acid.

[0009] This invention provides the application of reagents for detecting the metabolic markers in the preparation of kits for screening or diagnosing postoperative right heart failure in patients with hypertrophic obstructive cardiomyopathy or in the construction of a predictive system for postoperative right heart failure in patients with hypertrophic obstructive cardiomyopathy.

[0010] This invention provides a system for screening or diagnosing right heart failure after surgery for hypertrophic obstructive cardiomyopathy, comprising the following connected functional modules: The data acquisition module is used to acquire biomarker level data in user samples; The data analysis module is used to analyze the data acquired by the data acquisition module to obtain the risk of right heart failure after surgery for hypertrophic obstructive cardiomyopathy. The data output module is used to output the risk of right heart failure after surgery for hypertrophic obstructive cardiomyopathy, which is output by the data analysis module, to the display terminal. The biomarkers include the metabolic biomarkers.

[0011] Preferably, the biomarkers also include serum creatinine and pulmonary hypertension.

[0012] Preferably, the pulmonary hypertension refers to a mean pulmonary artery pressure >20 mmHg at rest.

[0013] Preferably, in analyzing the data acquired by the data acquisition module to obtain the risk result of right heart failure after hypertrophic obstructive cardiomyopathy surgery, the data analysis module is used to: input the data acquired by the data acquisition module into a prediction model based on the minimum absolute contraction and selection operator to analyze the risk result of right heart failure after hypertrophic obstructive cardiomyopathy surgery. The prediction model was constructed using 10-fold cross-validation.

[0014] This invention provides the application of the aforementioned metabolic markers, or in combination with serum creatinine and pulmonary hypertension, in constructing a diagnostic model of right heart failure after surgery for hypertrophic obstructive cardiomyopathy.

[0015] This invention provides the application of the aforementioned metabolic markers, or in combination with serum creatinine and pulmonary hypertension, in constructing a predictive model that distinguishes between right heart failure after surgery for hypertrophic obstructive cardiomyopathy and the absence of right heart failure after surgery for hypertrophic obstructive cardiomyopathy.

[0016] This invention provides a metabolic biomarker comprising at least two of the following metabolites: methylguanidine, D-glucuronic acid, 3-hydroxyoctanoic acid, phosphatidylcholine, and 3-hydroxytetradecenoic carnitine. This invention screened for the above five metabolites with significantly different levels from individuals with HOCM-RHF and those who did not develop right heart failure after surgery for hypertrophic obstructive cardiomyopathy (HOCM-nRHF). Experiments showed that a diagnostic model for HOCM-RHF based on the aforementioned metabolic biomarker had an AUC of 0.788 on the test set and 0.706 on the validation set. Therefore, preoperative detection of any two or more combinations of methylguanidine, D-glucuronic acid, 3-hydroxyoctanoic acid, phosphatidylcholine (18:1 (11Z) / 16:0), and 3-hydroxytetradecenoic carnitine has predictive value for HOCM-RHF patients undergoing ventricular septal myocardectomy.

[0017] This invention provides a system for screening or diagnosing right heart failure after surgery for hypertrophic obstructive cardiomyopathy (HOM), comprising the following connected functional modules: a data acquisition module for acquiring biomarker level data in user samples; a data analysis module for analyzing the data acquired by the data acquisition module to obtain a risk result of right heart failure after HOM; and a data output module for outputting the risk result of right heart failure after HOM to a display terminal. The biomarkers include metabolic biomarkers. The system rapidly and accurately diagnoses the risk of right heart failure after surgery in patients with HOM based on the levels of these metabolic biomarkers, exhibiting high reliability and providing a basis for personalized treatment.

[0018] The system provided by this invention further specifies that the biomarkers also include serum creatinine and pulmonary hypertension. This invention's research indicates that HOCM-PH patients are more prone to right heart failure, and preoperative pulmonary hypertension is also associated with an increased risk of postoperative right heart failure (RHF). Univariate analysis identified preoperative pulmonary hypertension, tricuspid systolic velocity, creatinine, systolic pulmonary artery pressure, estimated glomerular filtration rate (eGFR), right ventricular diameter (RVD), and triglycerides as predictors of postoperative right heart failure, with only pulmonary hypertension and creatinine retention showing a significant association with the occurrence of postoperative right heart failure in HOCM patients. The 5M+2M diagnostic model constructed based on the aforementioned biomarkers in this invention has an AUC of 0.850 on the test set and 0.795 on the validation set. This demonstrates that the 5M+2C diagnostic model constructed based on the aforementioned biomarkers exhibits excellent discriminative ability. Attached Figure Description

[0019] Figure 1 The model is a predictive model for postoperative right heart failure based on metabolites and clinical indicators. A shows the differential metabolites between the right heart failure (RHF) group and the non-RHF group in the derived cohort; B shows the five key metabolites selected by LASSO regression; C shows the histogram of regression coefficients for the five key metabolites; D shows the receiver operating characteristic (ROC) curves for predicting right heart failure using the five metabolites in the test set; E shows the receiver operating characteristic (ROC) curves for predicting right heart failure (RHF) using the five metabolites in the validation set; F shows the nomogram of the metabolite-clinical indicator combined model for predicting postoperative right heart failure. Figure 2This is a clinical-metabolite combined model for predicting postoperative right heart failure. A shows a comparison of Kaplan-Meier curves of postoperative right heart failure incidence between the non-pulmonary hypertension (nPH) and pulmonary hypertension (PH) groups; B shows forest plots and data tables of univariate and multivariate logistic regression analyses of postoperative right heart failure; C shows a box plot comparison of five key metabolite levels between the right heart failure and non-right heart failure groups in the derived cohort; D shows five... E. Comparison of receiver operating characteristic (ROC) curves for predicting postoperative right heart failure (RHF) using the five metabolite models (5M), the two clinical indicator models (2C), and the combined model (5M+2C); F. Comparison of Kaplan-Meier curves for the risk of right heart failure (RHF) between high-risk and low-risk patients in the test set after stratification based on the median nomogram scores of the five metabolite models (5M), the two clinical indicator models (2C), and the combined model (5M+2C); F. Comparison of Kaplan-Meier curves for the risk of right heart failure (RHF) between high-risk and low-risk patients in the validation set after stratification based on the median nomogram scores of the five metabolite models (5M), the two clinical indicator models (2C), and the combined model (5M+2C). Detailed Implementation

[0020] The present invention provides a metabolic marker comprising at least two of the following metabolites: methylguanidine, D-glucuronic acid, 3-hydroxyoctanoic acid, phosphatidylcholine, and 3-hydroxytetradecenoic carnitine.

[0021] In this invention, the metabolic biomarker preferably comprises a combination of two metabolites, a combination of three metabolites, or a combination of four metabolites. The metabolic biomarker preferably comprises at least one of the following: a first biomarker composed of methylguanidine and D-glucuronic acid; a second biomarker composed of methylguanidine and 3-hydroxyoctanoic acid; a third biomarker composed of methylguanidine and phosphatidylcholine; a fourth biomarker composed of methylguanidine and 3-hydroxytetradecanoic acid; a fifth biomarker composed of D-glucuronic acid and 3-hydroxyoctanoic acid; a sixth biomarker composed of D-glucuronic acid and phosphatidylcholine; a seventh biomarker composed of D-glucuronic acid and 3-hydroxytetradecanoic acid; and an eighth biomarker composed of 3-hydroxyoctanoic acid and phosphatidylcholine. The biomarker includes a ninth biomarker composed of 3-hydroxyoctanoic acid and 3-hydroxytetradecanoic acid, a tenth biomarker composed of phosphatidylcholine and 3-hydroxytetradecanoic acid; and preferably includes at least one of the following: an eleventh biomarker composed of methylguanidine, D-glucuronic acid and 3-hydroxyoctanoic acid, a twelfth biomarker composed of methylguanidine, D-glucuronic acid and phosphatidylcholine, a thirteenth biomarker composed of methylguanidine, D-glucuronic acid and 3-hydroxytetradecanoic acid, a fourteenth biomarker composed of methylguanidine, 3-hydroxyoctanoic acid and phosphatidylcholine, and a biomarker composed of methylguanidine, 3-hydroxyoctanoic acid and phosphatidylcholine. The biomarker comprises the fifteenth biomarker consisting of 3-hydroxytetradecanoic acid and 3-hydroxytetradecanoic acid; the sixteenth biomarker consists of methylguanidine, phosphatidylcholine, and 3-hydroxytetradecanoic acid; the seventeenth biomarker consists of D-glucuronic acid, 3-hydroxyoctanoic acid, and phosphatidylcholine; the eighteenth biomarker consists of D-glucuronic acid, 3-hydroxyoctanoic acid, and 3-hydroxytetradecanoic acid; the nineteenth biomarker consists of D-glucuronic acid, phosphatidylcholine, and 3-hydroxytetradecanoic acid; and the twentieth biomarker consists of 3-hydroxyoctanoic acid, phosphatidylcholine, and 3-hydroxytetradecanoic acid; preferably, it also includes at least one of the following: Group 21: Biomarker consisting of methylguanidine, D-glucuronic acid, 3-hydroxyoctanoic acid and phosphatidylcholine; Biomarker 22: Biomarker consisting of methylguanidine, D-glucuronic acid, 3-hydroxyoctanoic acid and 3-hydroxytetradecenoic acid; Biomarker 23: Biomarker consisting of methylguanidine, 3-hydroxyoctanoic acid, phosphatidylcholine and 3-hydroxytetradecenoic acid; Biomarker 24: Biomarker consisting of methylguanidine, D-glucuronic acid, phosphatidylcholine and 3-hydroxytetradecenoic acid; Biomarker 25: Biomarker consisting of D-glucuronic acid, 3-hydroxyoctanoic acid, phosphatidylcholine and 3-hydroxytetradecenoic acid.

[0022] In this invention, abnormal levels of metabolic markers in a sample reflect the occurrence of disease. Specifically, a trained model is used to predict right ventricular failure after hypertrophic obstructive cardiomyopathy surgery based on the levels of the aforementioned metabolic markers. Therefore, this application accurately predicts right ventricular failure after hypertrophic obstructive cardiomyopathy surgery based on specific levels of the aforementioned metabolic markers in the sample.

[0023] This invention provides the application of reagents for detecting the metabolic markers in the preparation of kits for screening or diagnosing postoperative right heart failure in patients with hypertrophic obstructive cardiomyopathy or in the construction of a predictive system for postoperative right heart failure in patients with hypertrophic obstructive cardiomyopathy.

[0024] In this invention, the reagents preferably include reagents for liquid chromatography-tandem mass spectrometry (LC-MS / MS) detection.

[0025] In this invention, methylguanidine and D-glucuronic acid, among the metabolic markers, were significantly elevated in HOCM-RHF, while 3-hydroxyoctanoic acid, phosphatidylcholine, and 3-hydroxytetradecenoic acid were significantly decreased. A diagnostic model was constructed based on individual metabolites, with a test set AUC of 0.69-0.72 and a validation set AUC of 0.55-0.64.

[0026] This invention provides a system for screening or diagnosing right heart failure after surgery for hypertrophic obstructive cardiomyopathy, comprising the following connected functional modules: The data acquisition module is used to acquire biomarker level data in user samples; The data analysis module is used to analyze the data acquired by the data acquisition module to obtain the risk of right heart failure after surgery for hypertrophic obstructive cardiomyopathy. The data output module is used to output the risk of right heart failure after surgery for hypertrophic obstructive cardiomyopathy, which is output by the data analysis module, to the display terminal. The biomarkers include the metabolic biomarkers.

[0027] In this invention, in analyzing the data acquired by the data acquisition module to obtain the risk of right heart failure after surgery for hypertrophic obstructive cardiomyopathy, the data analysis module is used to: input the data acquired by the data acquisition module into a prediction model based on the minimum absolute contraction and selection operator, and analyze the risk of right heart failure after surgery for hypertrophic obstructive cardiomyopathy. The prediction model is constructed using ten-fold cross-validation.

[0028] In this invention, the biomarkers preferably also include serum creatinine and pulmonary hypertension. Pulmonary hypertension is defined as a mean pulmonary artery pressure >20 mmHg at rest.

[0029] In this embodiment of the invention, experiments showed that serum creatinine and pulmonary hypertension were both significantly elevated in HOCM-RHF, and univariate hazard ratio results showed that only serum creatinine and pulmonary hypertension were significantly correlated with the occurrence of HOCM-RHF. A diagnostic model was constructed based on the serum creatinine and pulmonary hypertension, with a test set AUC of 0.747 and a validation set AUC of 0.724.

[0030] In this invention, when the biomarker simultaneously includes the metabolic marker, serum creatinine, and pulmonary hypertension, the constructed 5M+2M diagnostic model has an AUC of 0.850 on the test set and an AUC of 0.795 on the validation set.

[0031] This invention provides the application of the aforementioned metabolic markers, or in combination with serum creatinine and pulmonary hypertension, in constructing a diagnostic model of right heart failure after surgery for hypertrophic obstructive cardiomyopathy.

[0032] This invention provides the application of the aforementioned metabolic markers, or in combination with serum creatinine and pulmonary hypertension, in constructing a predictive model that distinguishes between right heart failure after surgery for hypertrophic obstructive cardiomyopathy and the absence of right heart failure after surgery for hypertrophic obstructive cardiomyopathy.

[0033] In this invention, the diagnostic model for right heart failure after surgery in patients with hypertrophic obstructive cardiomyopathy is preferably constructed using the minimum absolute contraction and selection operator. The diagnostic model is constructed using ten-fold cross-validation.

[0034] In this invention, the diagnostic method of the diagnostic model for right heart failure after hypertrophic obstructive cardiomyopathy surgery determines whether the hypertrophic obstructive cardiomyopathy patients from the sample will experience right heart failure after surgery based on the output results: when the output results are ≥ the threshold, the hypertrophic obstructive cardiomyopathy patients in the sample belong to the high-risk group for right heart failure after surgery. When the output result is less than the threshold, the patients with hypertrophic obstructive cardiomyopathy in the sample belong to the low-risk group for postoperative right heart failure.

[0035] In this invention, the threshold values ​​vary depending on the diagnostic model constructed using different biomarkers. For example, in the 5M+2C model constructed with five metabolic biomarkers and two biomarkers, the threshold is 174.1, with a sensitivity of 0.800 and a specificity of 0.821. In the 2C model constructed with two biomarkers, the threshold is 186.5, with a sensitivity of 0.650 and a specificity of 0.846. In the 5M model constructed with five metabolic biomarkers, the threshold is 166.5, with a sensitivity of 0.700 and a specificity of 0.772.

[0036] The following detailed description, in conjunction with embodiments, illustrates a biomarker for diagnosing postoperative right heart failure in hypertrophic obstructive cardiomyopathy and its application, but these should not be construed as limiting the scope of protection of this invention.

[0037] Example 1 A method for screening biomarkers for diagnosing hypertrophic obstructive cardiomyopathy complicated with pulmonary hypertension 1. Study population and data collection This retrospective cohort included 205 patients with hypertrophic obstructive cardiomyopathy (HOCM) and 57 healthy controls who underwent surgical ventricular septal myocardectomy at Fuwai Hospital, Chinese Academy of Medical Sciences, between January 2021 and December 2022. This cohort represents the surgically suitable population for hypertrophic obstructive cardiomyopathy. To ensure cohort homogeneity, patients with a history of alcohol-induced ventricular septal ablation, valvular surgery, or other concomitant cardiac conditions (such as congenital heart disease or right heart failure) were excluded. Peripheral plasma samples were collected upon admission for untargeted metabolomics analysis.

[0038] All patients signed written informed consent forms, agreeing to the use of their biological samples and anonymized clinical data for research purposes. The research protocol complies with the Declaration of Helsinki (1964).

[0039] 2.2 Follow-up Follow-up began with the initial postoperative assessment and continued until the endpoint event occurred. All patients were followed up at least annually via outpatient visits or telephone interviews. The final follow-up date was May 1, 2025. The composite endpoint included death, all-cause readmission, and right heart failure. Right heart failure (RHF) was defined as the presence of clinical symptoms such as peripheral edema, supported by echocardiographic evidence, and independently determined by three experienced physicians. Non-surgical survival was retrospectively calculated as the time from the initial diagnosis of HCM to surgical intervention. Among the HOCM patients, 29 developed right heart failure postoperatively, and 176 HOCM patients did not develop right heart failure postoperatively.

[0040] 2.3 Liquid Chromatography-Tandem Mass Spectrometry (LC-MS / MS) Analysis Plasma metabolomics analysis was performed using a Vanquish ultra-high performance liquid chromatography system coupled with a Q-Exactive HF mass spectrometer (Thermo Fisher Scientific). Metabolite separation was performed using a Hypersil GOLD C18 column (100 × 2.1 mm, 1.9 μm; Thermo Fisher Scientific) at 40 °C with a flow rate of 0.25 mL / min. Raw data were processed using CompoundDiscoverer version 3.1 (Thermo Fisher Scientific) with default parameters for peak detection and metabolite identification. Only mass-to-charge ratio (m / z) characteristics with a relative standard deviation (RSD) <30% and missing values ​​<20% from quality control (QC) samples were retained for subsequent analysis. A total of 339 metabolites were identified and included in subsequent analyses. The detailed method is described in the published literature (Cui, H., et al., Plasma Metabolites-Based Prediction in Cardiac Surgery-Associated Acute Kidney Injury. J Am Heart Assoc, 2021. 10(22): p. e021825.).

[0041] 2.4 Statistical Analysis Statistical analysis was performed using R (version 4.4.3). LASSO regression analysis was performed using the glmnet package, logistic regression model construction and visualization were performed using the rms package, and survival analysis was performed using the survminer package. Kyoto Encyclopedia of Genetics and Genomes (KEGG) pathway enrichment analysis was performed using MetaboAnalyst 6.0 (https: / / www.metaboanalyst.ca / ). Intergroup comparisons of metabolite levels were performed using the Mann-Whitney U test. (Two-tailed) p A value <0.05 is considered statistically significant.

[0042] 3.1 Metabolic diagnostic model of HOCM-PH By using stratified random sampling, the entire queue was divided into a test set (n=143) and a validation set (n=62), and the two sets of baseline features were comparable (Table 1).

[0043] Table 1 Sample Information and Test Results

[0044] Note: For categorical variables, the chi-square test was used.

[0045] 3.2 Metabolic Model for Precise Diagnosis of Right Heart Failure After HOCM-PH Surgery In the test set, selected P Metabolites with values ​​<0.05 were subjected to Lasso regression, yielding five metabolic biomarkers. Figure 1 (See Tables A–C and 2). Among patients who developed RHF postoperatively, methylguanidine and D-glucuronic acid were elevated, while 3-hydroxyoctanoic acid, PC (18:1(11Z) / 16:0) and 3-hydroxytetradecanoic acid ornithine were decreased. Figure 2 (C). When used alone, these metabolites had AUCs of 0.69–0.72 in the test set and 0.55–0.64 in the validation set. Figure 1 (Middle D–E).

[0046] Table 2. Detection results of five metabolic biomarkers in different cohorts.

[0047] 3.3 Metabolite-Clinical Combined Model for Precise Diagnosis of Right Heart Failure After HOCM Surgery Of the 204 cases treated with septal myotomy, although septal myotomy can alleviate pulmonary hypertension in patients with HOCM, a high risk of postoperative RHF was still observed (log-rank p = 0.042). Figure 2 (A). Univariate analysis identified preoperative PH, TVSV, creatinine, sPAP, eGFR, right ventricular diameter (RVD), and triglycerides as predictors of postoperative RHF. Among them, PH (HR = 2.66, P = 0.033) and creatinine (HR = 1.04, P = 0.006) is retained in the final multivariate model. Figure 2 (B)

[0048] Subsequently, a metabolite model (5M), a clinical model (2C), and a combined model (5M+2C) were constructed, and their predictive performance was evaluated. Figure 1 The combined 5M+2C model demonstrated excellent discrimination, with an AUC of 0.850 (95% CI: 0.756–0.943) for the derived cohort and 0.795 (95% CI: 0.652–0.937) for the validation cohort. Figure 2 (D). Based on the joint model, patients in the derived cohort were risk-scored and divided into high-risk and low-risk groups using the median as the cutoff. The high-risk group showed a significantly increased risk of RHF (log-rank p < 0.001). Figure 2 This was also confirmed in the verification queue (log-rank). p<0.001)( Figure 2 (F). These findings suggest that the 5M+2C model can effectively predict the potential risk of postoperative RHF in HOCM patients.

[0049] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A metabolic marker characterized in that, It includes at least two of the following metabolites: methylguanidine, D-glucuronic acid, 3-hydroxyoctanoic acid, phosphatidylcholine, and 3-hydroxytetradecenoic carnitine.

2. The metabolic marker of claim 1, wherein, It includes at least one of the following: a first biomarker composed of methylguanidine and D-glucuronic acid; a second biomarker composed of methylguanidine and 3-hydroxyoctanoic acid; a third biomarker composed of methylguanidine and phosphatidylcholine; a fourth biomarker composed of methylguanidine and 3-hydroxytetradecanoic acid; a fifth biomarker composed of D-glucuronic acid and 3-hydroxyoctanoic acid; a sixth biomarker composed of D-glucuronic acid and phosphatidylcholine; a seventh biomarker composed of D-glucuronic acid and 3-hydroxytetradecanoic acid; an eighth biomarker composed of 3-hydroxyoctanoic acid and phosphatidylcholine; a ninth biomarker composed of 3-hydroxyoctanoic acid and 3-hydroxytetradecanoic acid; and a tenth biomarker composed of phosphatidylcholine and 3-hydroxytetradecanoic acid.

3. The metabolic biomarker according to claim 1, characterized in that, Including at least one of the following: the eleventh biomarker composed of methylguanidine, D-glucuronic acid, and 3-hydroxyoctanoic acid; the twelfth biomarker composed of methylguanidine, D-glucuronic acid, and phosphatidylcholine; the thirteenth biomarker composed of methylguanidine, D-glucuronic acid, and 3-hydroxytetradecanoic acid; the fourteenth biomarker composed of methylguanidine, 3-hydroxyoctanoic acid, and phosphatidylcholine; the fifteenth biomarker composed of methylguanidine, 3-hydroxyoctanoic acid, and 3-hydroxytetradecanoic acid; and a biomarker composed of methylguanidine... The sixteenth biomarker consists of phosphatidylcholine and 3-hydroxytetradecanoic acid; the seventeenth biomarker consists of D-glucuronic acid, 3-hydroxyoctanoic acid and phosphatidylcholine; the eighteenth biomarker consists of D-glucuronic acid, 3-hydroxyoctanoic acid and 3-hydroxytetradecanoic acid; the nineteenth biomarker consists of D-glucuronic acid, phosphatidylcholine and 3-hydroxytetradecanoic acid; and the twentieth biomarker consists of 3-hydroxyoctanoic acid, phosphatidylcholine and 3-hydroxytetradecanoic acid.

4. The metabolic biomarker according to claim 1, characterized in that, It includes at least one of the following: the twenty-first biomarker consisting of methylguanidine, D-glucuronic acid, 3-hydroxyoctanoic acid and phosphatidylcholine; the twenty-second biomarker consisting of methylguanidine, D-glucuronic acid, 3-hydroxyoctanoic acid and 3-hydroxytetradecenoic acid; the twenty-third biomarker consisting of methylguanidine, 3-hydroxyoctanoic acid, phosphatidylcholine and 3-hydroxytetradecenoic acid; the twenty-fourth biomarker consisting of methylguanidine, D-glucuronic acid, phosphatidylcholine and 3-hydroxytetradecenoic acid; and the twenty-fifth biomarker consisting of D-glucuronic acid, 3-hydroxyoctanoic acid, phosphatidylcholine and 3-hydroxytetradecenoic acid.

5. The use of a reagent for detecting the metabolic markers described in any one of claims 1 to 4 in the preparation of a kit for screening or diagnosing postoperative right heart failure in patients with hypertrophic obstructive cardiomyopathy or in the construction of a predictive system for postoperative right heart failure in patients with hypertrophic obstructive cardiomyopathy.

6. A system for screening or diagnosing right heart failure after surgery for hypertrophic obstructive cardiomyopathy, characterized in that, Includes the following connected functional modules: The data acquisition module is used to acquire biomarker level data in user samples; The data analysis module is used to analyze the data acquired by the data acquisition module to obtain the risk of right heart failure after surgery for hypertrophic obstructive cardiomyopathy. The data output module is used to output the risk of right heart failure after surgery for hypertrophic obstructive cardiomyopathy, which is output by the data analysis module, to the display terminal. The biomarkers include the metabolic biomarkers described in any one of claims 1 to 4.

7. The system for screening or diagnosing right heart failure after hypertrophic obstructive cardiomyopathy according to claim 6, characterized in that, The biomarkers also include serum creatinine and pulmonary hypertension.

8. The system for screening or diagnosing right heart failure after hypertrophic obstructive cardiomyopathy according to claim 6 or 7, characterized in that, In analyzing the data acquired by the data acquisition module to obtain the risk results of right heart failure after hypertrophic obstructive cardiomyopathy surgery, the data analysis module is used to: input the data acquired by the data acquisition module into a prediction model based on the minimum absolute contraction and selection operator to analyze the risk results of right heart failure after hypertrophic obstructive cardiomyopathy surgery. The prediction model was constructed using 10-fold cross-validation.

9. The application of the metabolic markers or the combination of serum creatinine and pulmonary hypertension as described in any one of claims 1 to 4 in constructing a diagnostic model of right heart failure after hypertrophic obstructive cardiomyopathy surgery.

10. The use of the metabolic markers or the combination of serum creatinine and pulmonary hypertension as described in any one of claims 1 to 4 in constructing a predictive model to distinguish between right heart failure after surgery for hypertrophic obstructive cardiomyopathy and the absence of right heart failure after surgery for hypertrophic obstructive cardiomyopathy.

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