Biomarkers for calcific aortic valve disease and uses thereof

Abstract

The application discloses a biomarker of calcific aortic valve disease and application thereof, and belongs to the technical field of biological medicine. In the application, through non-targeted metabolomics discovery, serotonin deficiency can be a novel independent biomarker of calcific aortic valve stenosis. Specifically, receiver operating characteristic curve analysis shows that serotonin has good diagnostic efficiency for CAVD, and the optimal cutoff value is 148.2 ng / mL. Therefore, it is found in the application that the level of serotonin in CAVD patients is reduced, and the reduction is irrelevant to the severity of the disease, indicating that the serotonin has the potential to serve as a diagnostic biomarker.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to biomarkers for calcified aortic valve disease and their applications. Background Technology

[0002] The information disclosed in this background section is intended only to enhance understanding of the overall background of the invention and is not necessarily to be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art.

[0003] Calcific aortic valve disease (CAVD) is a progressive and serious condition involving an active, multi-stage process of leaflet inflammation, fibrosis, and mineralization, ultimately progressing from valvular sclerosis to severe, symptomatic aortic stenosis (AS). This progression not only impairs valvular function but also induces secondary myocardial changes, leading to heart failure. Current clinical management heavily relies on imaging, but this often only detects the disease after significant structural changes, such as calcification, have occurred. The topographical pattern of calcification itself is also a novel factor determining disease severity. Furthermore, the survival rate of patients with symptomatic aortic stenosis without valve replacement is extremely low, highlighting the importance of early detection. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a biomarker for calcific aortic valve disease and its applications. Currently, there remains a significant gap in the identification and validation of specific circulating metabolites that can reliably indicate the presence of CAVD (independent of its severity). This invention aims to fill this gap by integrating metabolomics approaches. By hypothesizing that CAVD is associated with a unique circulating metabolic profile, and that key metabolites within this profile can serve as independent diagnostic biomarkers, serotonin is derived as a novel diagnostic biomarker for CAVD.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows: In a first aspect, the present invention provides the use of biomarkers or substances for detecting said biomarkers in one or more of the following: a1) Prepare products for the diagnosis or auxiliary diagnosis of calcific aortic valve disease; a2) Prepare products for the prevention and / or treatment of calcific aortic valve disease; a3) Prepare products for screening calcific aortic valve disease; a4) To prepare products for predicting or assessing the risk of calcific aortic valve disease; a5) Prepare products for prognostic assessment of calcific aortic valve disease; The biomarker is serotonin (5-hydroxytryptamine, 5-HT).

[0006] Secondly, the present invention provides a product comprising a substance for detecting serotonin; the product having any one or more of the following uses: a1) Prepare products for the diagnosis or auxiliary diagnosis of calcific aortic valve disease; a2) Prepare products for the prevention and / or treatment of calcific aortic valve disease; a3) Prepare products for screening calcific aortic valve disease; a4) To prepare products for predicting or assessing the risk of calcific aortic valve disease; a5) Prepare products for prognostic assessment of calcific aortic valve disease.

[0007] Thirdly, the present invention provides a system for the diagnosis or auxiliary diagnosis of calcific aortic valve disease, the system comprising the following steps: (1) collecting a sample of a subject to be tested and collecting a control sample; (2) detecting and comparing the serotonin content in the sample of the subject to be tested and the control sample; wherein the serotonin content in the sample of the subject to be tested is lower than the serotonin content in the control sample.

[0008] Fourthly, the present invention provides a method for screening drugs for calcific aortic valve disease, comprising: c1) Treat the system expressing and / or containing the serotonin with the candidate substance; set up a parallel control without treatment with the candidate substance; c2) After completing step c1), detect the serotonin content in the system; if the serotonin content in the system treated with the candidate substance is significantly increased compared with the parallel control, the candidate substance can be used as a candidate drug for calcified aortic valve disease.

[0009] Fifthly, the present invention provides the use of substances that increase serotonin levels in the preparation of products, the functions of which include the prevention and / or treatment of calcific aortic valve disease.

[0010] In a sixth aspect, the present invention provides an apparatus for the detection, diagnosis, auxiliary diagnosis, screening, risk prediction, or prognostic assessment of calcific aortic valve disease, comprising a reagent for detecting serotonin levels and a computer-readable storage medium storing a computer program that causes a computer to perform the following steps: Calcification of aortic valve disease can be detected, diagnosed, aided in diagnosis, screened, risk predicted, or prognostic assessed based on serotonin levels.

[0011] In a seventh aspect, the present invention provides a system for prognostic assessment or auxiliary prognostic assessment of calcific aortic valve disease, the system comprising: b1) An analysis unit, the analysis unit comprising: a detection substance selected from serotonin content in a test sample of a subject; b2) An assessment unit comprising: performing a prognostic assessment of the subject based on the serotonin level determined in b1).

[0012] Eighthly, the present invention provides a product whose active ingredient includes a substance for increasing serotonin levels, the function of which includes prevention and / or treatment of calcified aortic valve disease.

[0013] In a ninth aspect, the present invention provides a method for detecting, diagnosing, preventing, treating, screening, assessing prognostic outcomes, or assisting in the assessment of calcific aortic valve disease.

[0014] The methods of prevention and / or treatment include: administering the above-mentioned products (such as drugs) into the body in a known manner.

[0015] The method for detection, diagnosis, screening, prognostic assessment, or auxiliary prognostic assessment includes the following steps: 1) obtaining detection data of biomarkers in the subject's biological samples, wherein the biomarkers include the aforementioned biomarkers; 2) processing the detection data using the aforementioned system, device, etc., to output relevant results for calcified aortic valve disease.

[0016] One or more of the above technical solutions have the following advantages or beneficial effects: In this invention, non-targeted metabolomics was used to discover that serotonin deficiency can be a novel independent biomarker for calcific aortic stenosis.

[0017] Specifically, metabolomics analysis identified 810 metabolites, of which lipids accounted for 71.7%. Based on specific criteria, 70 differentially expressed metabolites were screened. Serotonin levels were significantly reduced in CAVD patients, and pathway analysis revealed significant dysregulation of the serotonin receptor signaling pathway. A random forest model highlighted serotonin as a key categorical variable. In an independent validation cohort of 303 individuals, serotonin levels were lower in the CAVD group compared to the control group, but there were no differences between CAVD groups (different severities). Multivariate analysis determined that low serotonin levels were an independent predictor of CAVD. Receiver operating characteristic (ROC) curve analysis showed that serotonin has good diagnostic efficacy for CAVD, with an optimal cutoff value of 148.2 ng / mL. Therefore, this invention reveals that serotonin levels are reduced in CAVD patients, and this reduction is independent of disease severity, indicating its potential as a diagnostic biomarker. Attached Figure Description

[0018] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0019] Figure 1 Principal component analysis (PCA) score plot for metabolomics analysis of serum samples from CAVD patients and matched controls; Figure 2 This is a partial least squares discriminant analysis (PLS-DA) score plot in metabolomics analysis; Figure 3 For permutation tests in metabolomics analysis; Figure 4 HMDB-based classification of identified metabolites in metabolomics analysis; Figure 5 The CAVD (Cardiform Diagnostic and Activated Pathways) data are as follows: A represents the number of significantly altered metabolic ions; B represents the number of significantly altered differential secondary metabolites, with screening parameters of p-value < 0.05, fold change > 1.2, and variable importance projection (VIP) score > 1 from PLS-DA analysis; C represents the volcano plot of characteristic differential secondary metabolites, with screening parameters of p-value < 0.05, fold change > 1.2, and variable importance projection (VIP) score > 1 from PLS-DA analysis; and D represents the top ten most significantly enriched KEGG pathways. Figure 6 To validate key metabolites and serotonin levels in CAVD; where A is random forest analysis and B is serotonin concentration in the validation cohort; Figure 7 This study aims to analyze the association, subgroup analysis, and diagnostic efficacy of serotonin in CAVD. A represents smooth curve fitting analysis, after adjusting for relevant confounding factors (hemoglobin, PLT, TG, PLA2, LP(a), FFA, CR, UA, eGFR), and a generalized additive model is used for smooth curve fitting to analyze the relationship between serotonin levels and CAVD. The red solid line represents the probability of CAVD occurrence, and the shaded area represents the 95% confidence interval curve. B represents subgroup analysis, and C represents ROC analysis of the diagnostic incidence of CAVD. Model 1: Serotonin level; Model 2: Hemoglobin, PLT, TG, PLA2, LP(a), FFA, CR, UA, eGFR; Model 3: Model 2 + serotonin. Figure 8 This is a graph showing the high correlation coefficients observed between QC samples. Detailed Implementation

[0020] Explanation of terms involved: AVS, aortic stenosis; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; CAVD, calcific aortic valve disease; CR, creatinine; CRP, C-reactive protein; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; ESR, erythrocyte sedimentation rate; FBG, fasting blood glucose; FFA, free fatty acids; Hb, hemoglobin; Hcy, homocysteine; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; LP(a), lipoprotein(a); PLA2, phospholipase A2; Neutralocyte count; PLT, platelets; RBC, red blood cell count; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides; UA, uric acid; WBC, white blood cell count.

[0021] Model 1: Serotonin level; Model 2: Hemoglobin, PLT, TG, PLA2, LP(a), FFA, CR, UA, eGFR; Model 3: Model 2 + serotonin. ROC curve, receiver operating characteristic curve; AUC, area under the ROC curve.

[0022] AUC, Area Under the ROC Curve; NRI, Net Reclassification Improvement Index; IDI, Overall Discrimination Improvement Index.

[0023] Calcific aortic valve disease (CAVD) is a progressive and serious condition. Without valve replacement, symptomatic aortic stenosis has a very low survival rate, highlighting the importance of early detection. The pathogenesis of CAVD is multifactorial, driven by chronic inflammation, lipid infiltration (including lipoprotein(a) with a proven causal relationship), oxidative stress, and osteogenic differentiation of valvular interstitial cells. These processes lead to the deposition of calcium and phosphate crystals within the valve leaflets, similar to bone formation. Metabolic reprogramming is considered to play a crucial role in this pathophysiological process. Metabolomics provides a global, non-targeted profile of small-molecule metabolites, making it a powerful tool for revealing such systemic biochemical changes in disease states. It captures functional endpoints of cellular processes, provides unique metabolic signatures reflecting underlying pathology, and serves as a rich source for discovering novel biomarkers. For example, studies have shown that numerous metabolites distinguishing patients with aortic stenosis from controls are associated with nitric oxide metabolism and inflammatory pathways, consistent with known disease mechanisms.

[0024] Currently, there remains a significant gap in the identification and validation of specific circulating metabolites that can reliably indicate the presence of CAVD (independent of its severity). This study aims to discover novel metabolic biomarkers for CAVD using non-targeted metabolomics techniques.

[0025] In the discovery phase, CAVD patients and matched controls were included, and their serum metabolites were profiled. Key metabolites were identified using KEGG pathway analysis and a random forest algorithm, with serotonin (5-HT) selected as the primary candidate, and validated in a large cohort containing different CAVD severities. Logistic regression and subgroup analysis were further used to explore the relationship between serotonin and CAVD.

[0026] The results showed that the cohort included 58 participants, comprising 29 patients with chronic amenorrhea (CAVD) (mean age 69.7 years; 44.8% male) and 29 controls (mean age 66.8 years; 58.6% male). Metabolomics analysis identified 810 metabolites, of which lipids accounted for 71.7%. 70 differentially expressed metabolites were screened according to specific criteria. Serotonin levels were significantly reduced in CAVD patients, and pathway analysis revealed significant dysregulation of the serotonin receptor signaling pathway. A random forest model highlighted serotonin as a key categorical variable. In an independent validation cohort of 303 participants, serotonin levels were lower in the CAVD group compared to the control group, but there were no differences between CAVD groups (different severities). Multivariate analysis determined that low serotonin levels were an independent predictor of CAVD. Receiver operating characteristic (ROC) curve analysis showed that serotonin had good diagnostic efficacy for CAVD, with an optimal cutoff value of 148.2 ng / mL.

[0027] Therefore, this study found that CAVD patients had reduced serotonin levels, and this reduction was not related to the severity of the disease, suggesting that it has the potential to serve as a diagnostic biomarker.

[0028] In this invention, the area under the curve (AUC) of Model 1 (serotonin only) was 0.7742 (95% CI: 0.7172~0.8312, P<0.001). The optimal cutoff value for serotonin was determined to be 140~150 ng / mL (preferably 148.2 ng / mL), with a sensitivity greater than 80% (preferably 83.7%) and a specificity greater than 60% (preferably 60.4%). Compared with Model 2 (AUC = 0.7489, 95% CI: 0.6937~0.8041, P<0.001), the AUC of Model 3, with the addition of serotonin, was significantly increased to 0.8626 (95% CI: 0.8203~0.9049, P<0.001).

[0029] In one typical embodiment, the present invention provides the application of biomarkers or substances for detecting said biomarkers in one or more of the following: a1) Prepare products for the diagnosis or auxiliary diagnosis of calcific aortic valve disease; a2) Prepare products for the prevention and / or treatment of calcific aortic valve disease; a3) Prepare products for screening calcific aortic valve disease; a4) To prepare products for predicting or assessing the risk of calcific aortic valve disease; a5) Prepare products for prognostic assessment of calcific aortic valve disease; The biomarker is serotonin (5-HT).

[0030] The substance used to detect the biomarker is a reagent for detecting serotonin levels. The reagent is used to detect serotonin levels in a sample. The sample includes at least one of whole blood, serum, plasma, and tissue fluid.

[0031] Methods for detecting biomarkers include: enzyme-linked immunosorbent assay (ELISA), immunofluorescence assay, radioimmunoassay, immunoprecipitation assay, immunoblotting, high performance liquid chromatography (HPLC), capillary gel electrophoresis, near-infrared spectroscopy, mass spectrometry, immunochemiluminescence assay, colloidal gold immunochromatography, fluorescence immunochromatography, surface plasmon resonance (SPR), immuno-PCR, or biotin-avidin assay.

[0032] The products mentioned include chips, reagents, test strips, drugs, formulations, reagent kits, high-throughput screening platforms, or biosensors.

[0033] The diagnostic or auxiliary diagnostic product is used to diagnose and identify calcified aortic valve disease, including calcified aortic valve disease; and / or, the calcified aortic valve disease diagnostic product is used for the staging diagnosis of calcified aortic valve disease.

[0034] Biomarkers can be used to construct risk prediction models for calcific aortic valve disease.

[0035] In one typical embodiment, the present invention provides a product comprising the aforementioned substance for detecting serotonin levels; the product has any one or more of the following uses: a1) Prepare products for the diagnosis or auxiliary diagnosis of calcific aortic valve disease; a2) Prepare products for the prevention and / or treatment of calcific aortic valve disease; a3) Prepare products for screening calcific aortic valve disease; a4) To prepare products for predicting or assessing the risk of calcific aortic valve disease; a5) Prepare products for prognostic assessment of calcific aortic valve disease.

[0036] The products include reagent kits, protein chips, immunochromatographic diagnostic strips, high-throughput sequencing platforms, or biosensors.

[0037] CAVD patients have decreased serotonin levels, and this decrease is not related to the severity of the disease.

[0038] In one typical embodiment, the present invention provides a system for the diagnosis or auxiliary diagnosis of calcific aortic valve disease, the system comprising the following steps: (1) collecting a sample from a subject to be tested and collecting a control sample; (2) detecting and comparing the serotonin content in the sample from the subject to be tested and the control sample; wherein the serotonin content in the sample from the subject to be tested is lower than the serotonin content in the control sample.

[0039] The control samples are derived from healthy individuals or healthy tissues of the subjects to be tested. The samples from the subjects to be tested are one or more of the following: serum, plasma, whole blood, pus, organs, biopsy samples, circulating tumor cells, circulating tumor DNA, or exfoliated cells from urine.

[0040] In one typical implementation, the present invention provides a method for screening drugs for calcific aortic valve disease, comprising: c1) Treat the system expressing and / or containing the serotonin with the candidate substance; set up a parallel control without treatment with the candidate substance; c2) After completing step c1), detect the serotonin content in the system; if the serotonin content in the system treated with the candidate substance is significantly increased compared with the parallel control, the candidate substance can be used as a candidate drug for calcified aortic valve disease.

[0041] If the candidate substance can upregulate serotonin levels, it indicates that it is a potential preventive or therapeutic agent.

[0042] In another specific embodiment of the present invention, the system may be a cell system, a subcellular system, a solution system, a tissue system, an organ system, or an animal system.

[0043] In another specific embodiment of the present invention, the tissue in the tissue system can be tissue from calcified aortic valve disease.

[0044] In another specific embodiment of the present invention, the animals in the animal system can be mammals, such as rats, mice, guinea pigs, rabbits, monkeys, humans, etc.

[0045] In one typical embodiment, the present invention provides the use of substances that increase serotonin levels in the preparation of products; Among them, substances that increase serotonin levels may include RNA interference molecules or antisense oligonucleotides that target serotonin, small molecule inhibitors, siRNA, and substances that carry out lentiviral infection or gene knockout.

[0046] The products mentioned can be medicines, food, etc.

[0047] In one typical embodiment, the present invention provides an apparatus for the detection, diagnosis, auxiliary diagnosis, screening, risk prediction, or prognostic assessment of calcific aortic valve disease, comprising a reagent for detecting serotonin levels and a computer-readable storage medium storing a computer program that causes a computer to perform the following steps: Calcification of aortic valve disease can be detected, diagnosed, aided in diagnosis, screened, risk predicted, or prognostic assessed based on serotonin levels.

[0048] In one typical embodiment, the present invention provides a system for prognostic assessment or auxiliary prognostic assessment of calcific aortic valve disease, the system comprising: b1) An analysis unit, the analysis unit comprising: a detection substance selected from serotonin content in a test sample of a subject; b2) An assessment unit comprising: performing a prognostic assessment of the subject based on the serotonin level determined in b1).

[0049] In one typical embodiment, the present invention provides a product whose active ingredient includes a substance for increasing serotonin levels.

[0050] The product's functions include the prevention and / or treatment of calcific aortic valve disease.

[0051] According to the present invention, the concept of "treatment" means any measure applicable to the treatment of calcific aortic valve disease, or preventive treatment of the disease or its symptoms, or prevention of recurrence of the disease, such as recurrence after the end of a treatment period or treatment of symptoms of an existing disease, or preemptive intervention to prevent, suppress or reduce the occurrence of such disease or symptoms.

[0052] According to the present invention, the above-mentioned drug further includes at least one inactive pharmaceutical ingredient. The inactive pharmaceutical ingredient may be a carrier, excipient, or diluent commonly used in pharmaceuticals. Furthermore, it can be formulated into dosage forms such as powders, granules, tablets, capsules, suspensions, emulsions, syrups, and sprays, in the form of oral, topical, suppository, and sterile injectable solutions, according to conventional methods. The inactive pharmaceutical ingredients such as carriers, excipients, and diluents that may be included are well known in the art, and those skilled in the art can determine that they meet clinical standards. The carriers, excipients, and diluents include, but are not limited to, lactose, glucose, sucrose, sorbitol, mannitol, xylitol, erythritol, maltitol, starch, gum arabic, alginate, gelatin, calcium phosphate, calcium silicate, cellulose, methylcellulose, microcrystalline cellulose, polyvinylpyrrolidone, water, methylparaben, propylparaben, talc, magnesium stearate, and mineral oil.

[0053] In one typical implementation, the present invention provides a method for detecting, diagnosing, preventing, treating, screening, assessing prognosis, or assisting in the prognostic assessment of calcific aortic valve disease.

[0054] The methods of prevention and / or treatment include administering the aforementioned product (such as a drug) into the body via known means. For example, delivery via intravenous systemic delivery or local injection to the appropriate tissue. Optionally, administration may be made via intravenous, percutaneous, intranasal, mucosal, or other delivery methods. Such administration may be performed via a single dose or multiple doses. Those skilled in the art will understand that the actual dose to be administered in this invention can vary considerably depending on various factors, such as target cells, biological type or tissue, the general condition of the subject being treated, route of administration, manner of administration, etc. The drug may be administered to humans and non-human mammals, such as mice, rats, guinea pigs, rabbits, dogs, monkeys, chimpanzees, etc.

[0055] The method for detection, diagnosis, screening, prognostic assessment, or auxiliary prognostic assessment includes the following steps: 1) obtaining detection data of biomarkers in the subject's biological samples, wherein the biomarkers include the aforementioned biomarkers; 2) processing the detection data using the aforementioned system, device, etc., to output relevant results for calcified aortic valve disease.

[0056] In this invention, unless otherwise specified, all other test materials and instruments are conventional test materials in the field and can be purchased through commercial channels.

[0057] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below with reference to specific embodiments.

[0058] Example 1 1. Research Design and Participants This case-control study aimed to investigate metabolic alterations in cerebral aortic valve disease (CAVD). Participants were enrolled at Qilu Hospital of Shandong University from February to April 2024. The study was divided into a discovery group and a validation group. Serotonin was screened using metabolomics data (N=58), and then validated using a large blood sample (N=303). Specialist clinicians made diagnoses based on echocardiographic results and assigned participants to either the CAVD group (n=29) or the control group (n=29). During the validation phase, prospectively and consecutively enrolled CAVD patients who visited the hospital between August 2024 and June 2025, and divided them into two groups: 101 patients with severe calcific aortic stenosis, 101 patients with mild to moderate calcific aortic stenosis, and 101 individuals forming the control group. Age- and sex-matched individuals with no evidence of aortic valve disease on echocardiographic examination were recruited concurrently as controls.

[0059] The inclusion criteria for non-severe (mild to moderate) CAVD are as follows: (1) age 30-90 years; (2) echocardiographic diagnosis of mild or moderate CAVD based on aortic valve orifice area: aortic valve orifice area (AVA) ≥1 cm 2 and ≤3 cm 2 Or, the maximum aortic valve velocity (AVVmax) is ≥ 2.5 m / s and < 4.0 m / s, or the mean aortic valve transvalvular pressure gradient (mean AVG) is > 0 mmHg and < 40 mmHg.

[0060] The inclusion criteria for severe CAVD are as follows: (1) age 30-90 years; (2) echocardiographic diagnosis of severe CAVD: AVA <1 cm 2 AV Vmax ≥ 4.0 m / s, or average AVG ≥ 40 mmHg.

[0061] The control group met the following criteria: (1) age 30-90 years; (2) echocardiography confirming normal aortic valve structure and function. The exclusion criteria were: (1) severe liver failure (alanine aminotransferase [ALT] > 10 times the upper limit of normal) or renal failure (estimated glomerular filtration rate [eGFR] < 30); (2) rheumatic heart disease; (3) infective endocarditis; (4) history of heart valve replacement or repair; (5) connective tissue disease; (6) history of malignant tumors.

[0062] This study complied with the Declaration of Helsinki and was approved by the Research Ethics Committee of Qilu Hospital, Shandong University (Approval No.: KYLL-2025(ZM)-906). All participants provided written informed consent.

[0063] 2. Data Collection This study collected the following five types of data: (1) Demographic data: baseline age (years, continuous variable), sex (female and male).

[0064] (2) Smoking status (never or never).

[0065] (3) Medical history and medication use (yes or no): hypertension, diabetes, and angiotensin-converting enzyme inhibitors / angiotensin II receptor antagonists, beta-blockers, calcium channel blockers, diuretics, hypoglycemic agents, antiplatelet drugs and statins.

[0066] (4) Body measurement indicators: systolic blood pressure (SBP), diastolic blood pressure (DBP) and body mass index (BMI).

[0067] (5) Laboratory test data: white blood cell count (WBC), red blood cell count (RBC), hemoglobin (Hb), platelet count (PLT), neutrophil count (NeuT), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), alanine aminotransferase (ALT), aspartate aminotransferase (AST), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), lipoprotein (a) (Lp(a)), homocysteine ​​(Hcy), free fatty acids (FFA), phospholipase A2 (PLA2), fasting blood glucose (FBG), creatinine (CR), uric acid (UA), estimated glomerular filtration rate (eGFR).

[0068] 3. Non-targeted metabolomics detection: The collected samples were thawed on ice to prepare sample extracts, and then the metabolites were extracted using 80% methanol solution. Briefly, 100 μL of each sample was mixed with 400 μL of pre-chilled methanol. The resulting mixture was incubated at -20°C for 30 min. After centrifugation at 20,000 × g for 15 min, the supernatant was transferred to a new tube and vacuum dried. The dried extract was then reconstituted in 100 μL of 80% methanol and stored at -80°C until analysis by liquid chromatography-mass spectrometry (LC-MS). Furthermore, a pooled quality control (QC) sample was prepared by combining 10 μL aliquots from each extraction mixture.

[0069] All chromatographic separations were performed using an ultra-high performance liquid chromatography (UPLC) system (SCIEX, UK). Metabolites eluted from the column were detected using a TripleTOF5600plus high-resolution tandem mass spectrometer (SCIEX, UK). The mass spectrometer was operated in both positive and negative ion modes. Mass accuracy calibration was performed every 20 samples during acquisition. To validate and maintain data quality, samples were analyzed in random order, and a QC sample was inserted every 8 samples analyzed in the data acquisition sequence. Raw LC / MS and untargeted metabolomics data were obtained by Hangzhou Lianchuan Biotechnology Co., Ltd. (Hangzhou, Zhejiang Province, China).

[0070] Raw LC-MS data files were converted to mzXML format and then processed using the XCMS, CAMERA, and metaX toolboxes in the R software environment. Metabolite ions were identified based on a combination of retention time and precise mass-to-charge ratio (m / z). Metabolites were annotated by matching experimentally obtained m / z values ​​with the KEGG and HMDB databases. The identification results were further validated using an internal fragment spectral library. Peak intensity data were preprocessed using the metaX toolbox. Principal component analysis (PCA) was applied to the preprocessed dataset to identify potential outliers and assess batch effects. A robust LOESS signal correction-based quality control method was applied to QC samples according to the injection order to correct for signal intensity drift over time. Metabolic features with a relative standard deviation (RSD) exceeding 30% in all QC samples were excluded from subsequent analyses. Student's t-test was used to assess differences in metabolite levels between groups. Partial least squares discriminant analysis (PLS-DA) was performed after logarithmic transformation and unit variance (UV) scaling. Metabolites with variable importance projection (VIP) scores greater than 1 and t-test p-values ​​less than 0.05 from the PLS-DA model were considered significantly altered. The robustness of the PLS-DA model was assessed using a permutation test of 200. Pathway enrichment analysis was performed on significantly altered metabolites using MetaboAnalyst (http: / / www.metaboanalyst.ca) to identify potentially enriched metabolic pathways. The functional roles of these metabolites in various biological pathways were further interpreted using the KEGG database (https: / / www.kegg.jp).

[0071] 4. Serum test: Blood samples were collected via venipuncture according to a standardized protocol. Serum 5-hydroxytryptamine (5-HT) concentrations were quantified using a commercial ELISA kit (E-EL-0033, Elabscience, Wuhan, China) according to the manufacturer's instructions. Samples were appropriately diluted based on a standard curve. Optical density (OD) at 450 nm was measured using an automated microplate reader, and serotonin concentrations were calculated by interpolation. All samples were analyzed in triplicate, and statistical analysis was performed using the mean values ​​to minimize variability and ensure reliability.

[0072] 5. Statistical Analysis: Statistical analyses were performed using SPSS 29.0 (SPSS Inc., Chicago, IL, USA), R version 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria), and GraphPad Prism 10 (GraphPad Software Inc., San Diego, CA, USA). Normality and homogeneity of variance were tested for continuous variables. Normally distributed data were expressed as mean ± standard deviation (SD) and compared using one-way ANOVA; non-normally distributed data were expressed as median (interquartile range) and analyzed using the Bonferroni-corrected Kruskal-Wallis test. Categorical variables were summarized as frequencies (percentages) and compared using the chi-square test or Fisher's exact test, with Bonferroni correction. Spearman rank correlation was used to assess associations between non-normally distributed variables. To explore the relationship between serotonin and CAVD, univariate logistic regression was performed, followed by multivariate logistic regression, adjusting for potential confounding factors, to determine whether serotonin was an independent predictor of CAVD. Multicollinearity was assessed using the variance inflation factor (VIF). The dose-response relationship between serotonin and CAVD was characterized using smooth curve fitting. Subgroup analyses were performed based on age, PLT, HDL-C, LDL-C, TG, LP(a), FFA, and PLA2 levels, and interaction p-values ​​were calculated to assess effect modification. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic efficacy of serotonin for the presence and severity of CAVD. The optimal cutoff value was determined by maximizing the Youden index (J = sensitivity + specificity - 1). The net reclassification improvement index (NRI) and integrated discriminant improvement index (IDI) were calculated to assess risk stratification. A two-sided p-value <0.05 was considered statistically significant.

[0073] 6. Results Analysis: 6.1 Baseline characteristics of participants: The cohort included 58 participants: 29 patients with severe CAVD and 29 age- and sex-matched control patients. As shown in Table 1, CAVD patients had significantly lower hemoglobin levels compared to the control group, while other clinical characteristics were not significantly different. In the external validation phase, an independent cohort of 303 participants was enrolled. As shown in Table 2, baseline characteristics were well-balanced across all groups, with no significant differences in demographic variables or clinical comorbidities.

[0074] Table 1. Baseline characteristics of participants in the cohort were identified.

[0075] Table 2 Baseline characteristics of participants in the independent validation cohort

[0076] 6.2 Serum metabolic profile of calcific aortic valve disease: After data preprocessing and batch normalization, 6,552 and 4,048 precursor molecules were detected in positive and negative ion modes, respectively. Quality control was assessed by Pearson correlation analysis of the abundance values ​​of all QC samples. High correlation coefficients were observed among the QC samples. Figure 8 This indicates excellent reproducibility and instrument stability throughout the entire analysis sequence, confirming that the data quality is suitable for downstream statistical analysis.

[0077] Figures 1-4 To perform metabolomics analysis on serum samples from CAVD patients and matched controls, Figure 1 Principal Component Analysis (PCA) score plot. Figure 2 This is a partial least squares discriminant analysis (PLS-DA) score plot. Figure 3 For permutation testing, Figure 4 Classification of identified metabolites based on HMDB.

[0078] Principal component analysis (PCA) showed significant clustering within both the CAVD group and the control group. Figure 1 This indicates that the metabolite composition within each group is similar. In contrast, partial least squares discriminant analysis (PLS-DA) clearly separates the two groups. Figure 2 This supports the existence of significant metabolic differences between groups. The permutation test confirms that the PLS-DA model is not overfitting. Figure 3 ).

[0079] Metabolite annotation was performed by comparing precise molecular mass data with the KEGG and HMDB databases. The classification and functional annotation of identified metabolites were visualized based on both databases. HMDB-based classification ( Figure 4 The results indicate that "lipids and lipid molecules" are the most abundant category of metabolites.

[0080] 6.3 Accumulation of differentially metabolites and pathways: Based on the criteria of p-value < 0.05, fold change > 1.2, and variable importance projection (VIP) score > 1 obtained from PLS-DA analysis, 707 significantly altered metabolic ions were identified. Figure 5 (A) and 70 secondary metabolites ( Figure 5 (B and C in the middle).

[0081] Pathway enrichment analysis of these metabolites revealed ten significantly enriched KEGG pathways, including cAMP signaling, serotonin receptor agonists / antagonists, primary bile acid biosynthesis, bile secretion, axonal regeneration, cholesterol metabolism, choline metabolism in cancer, gap junctions, synaptic vesicle circulation, and endocytosis. Figure 5 (D in the study). Among them, the pathways associated with serotonin receptor agonists / antagonists showed the highest enrichment fraction, suggesting that they may play a prominent role in the context of this study.

[0082] 6.4 Serotonin is a key metabolite in CAVD: To identify key metabolites associated with CAVD, random forest analysis was performed. Figure 6 (A) This method highlighted several significantly altered metabolites in the CAVD group, including octadecanoamide, oleamide, 25-hydroxycholesterol, 2-hexenylcarnitine, succinic acid, indole-3-acrylic acid, and linolenic acid. Notably, indole-3-acrylic acid is a metabolic derivative of serotonin. Combined with the KEGG pathway enrichment results, this finding further emphasizes the potentially important role of serotonin in CAVD.

[0083] Analysis showed that serotonin concentrations were significantly lower in both the severe and non-severe CAVD groups compared to the control group (control group: 177.6 ± 68.7 ng / mL; non-severe AVS group: 92.1 ± 17.5 ng / mL; severe AVS group: 82.1 ± 26.5 ng / mL; P<0.001). However, no statistically significant difference in serotonin levels was observed between the severe and non-severe CAVD groups. Figure 6 (B in the middle).

[0084] 6.5 Univariate and Multivariate Logistic Regression Analysis: Univariate and multivariate logistic regression analyses were performed to identify factors associated with CAVD. Univariate analysis showed that CAVD was significantly associated with several factors, including medication history (calcium channel blockers, diuretics, and hypoglycemic agents), red blood cell count, hemoglobin, platelet count, triglycerides, phospholipase A2, LP(a), free fatty acids, creatinine, uric acid, estimated glomerular filtration rate, and serotonin. Considering the collinearity between red blood cell count and hemoglobin, only hemoglobin was retained for subsequent adjustment. Multivariate analysis adjusted for hemoglobin, PLT, CR, UA, eGFR, TG, PLA2, LP(a), FFA, and serotonin. The results showed that serum 5-HT (OR = 0.985, 95% CI: 0.981–0.989, p<0.001), PLT (OR = 0.992, 95% CI: 0.987–0.998, p<0.05), TG (OR = 0.613, 95% CI: 0.401–0.936, p<0.05), LP(a) (OR = 1.009, 95% CI: 1.004–1.014, p<0.05), FFA (OR = 1.019, 95% CI: 1.003–1.036, p<0.05) and PLA2 (OR = 1.005, 95% CI: 1.002–1.008, p<0.05) were independent predictors of CAVD (Table 3). Furthermore, smooth curve fitting was used to characterize the relationship between serotonin levels and CAVD, and the results showed a linear association. Figure 7 (A in the middle).

[0085] Table 3. Univariate and multivariate logistic regression analysis of independent influencing factors of AVS

[0086] Note: The blank parts in Table 3 are factors that were not included in the multivariate analysis, and the bolded parts are factors that are still statistically significant after multivariate regression analysis.

[0087] 6.6 Subgroup Analysis: like Figure 7 As shown in B, the association between elevated serotonin levels and reduced risk of CAVD was largely consistent across subgroups. No significant interactions were observed between serotonin and age, PLT, and HDL-C, LDL-C, TG, PLA2, FFA, and LP(a) levels (all P values ​​> 0.200), indicating that serotonin remains an independent predictor of CAVD across different subgroups.

[0088] 6.7 Independent diagnostic efficacy and added value of serotonin: To evaluate the diagnostic utility of serotonin for CAVD, study participants were divided into a control group and a CAVD group (the latter including non-severe and severe AVS cases). Three diagnostic models were established: Model 1 included only serotonin levels; Model 2 included variables that were significant in univariate analysis (P<0.05) but excluded serotonin; Model 3 was a combination of both, i.e., adding serotonin to Model 2. The area under the curve (AUC) for Model 1 (serotonin only) was 0.7742 (95% CI: 0.7172–0.8312, P<0.001). The optimal cutoff value for serotonin was determined to be 148.2 ng / mL, with a sensitivity of 83.7% and a specificity of 60.4%. Compared with Model 2 (AUC = 0.7489, 95% CI: 0.6937–0.8041, P<0.001), the AUC of Model 3 was significantly increased to 0.8626 (95% CI: 0.8203–0.9049, P<0.001) after the addition of serotonin. Figure 7 (C) In addition, the discrimination and reclassification abilities of models with and without serotonin in diagnosing the presence and severity of CAVD were evaluated. After adding serotonin, AUC, Net Reclassification Improvement Index (NRI), and Integrated Discriminant Improvement Index (IDI) were all statistically significantly improved (Table 4). These results indicate that serotonin has strong independent and additional diagnostic value in identifying the occurrence of CAVD.

[0089] Table 4. Prognostic potential of serotonin increments

[0090] 7. Discussion This metabolomics study yielded several key findings. First, patients with CAVD exhibit systemic metabolic disorders, with serotonin identified as a key discriminant metabolite. Second, serotonin levels in CAVD patients are significantly and progressively decreased, with the most pronounced reduction observed in severe cases. Third, decreased serotonin levels were identified as a robust and independent predictor of the presence of CAVD. In conclusion, these findings identify circulating serotonin as a potential biomarker for CAVD, potentially aiding in screening and risk stratification.

[0091] The data highlight the central role of lipid dysregulation in CAVD, with "lipids and lipid molecules" constituting the most significantly altered class of metabolites. Enrichment of pathways associated with primary bile acid biosynthesis and cholesterol metabolism further reinforces this, indicating profound disruption of lipid homeostasis. Dyslipidemia, particularly elevated LDL-C and Lp(a), is a recognized risk factor for CAVD progression. Lp(a) is a genetically influenced particle that primarily mediates pathological processes through the oxidized phospholipids (OxPLs) it carries. These OxPLs are transported to valvular tissue and trigger pro-inflammatory and osteogenic signaling in valvular interstitial cells (VICs). Furthermore, bile acids, as signaling molecules, regulate inflammation and calcium metabolism—processes associated with CAVS—through receptors such as the farnesoid X receptor (FXR) and G protein-coupled bile acid receptor (TGR)5. Evidence regarding the unique bile acid profile in calcified valves and its correlation with disease severity supports this view.

[0092] Serotonin is a biogenic amine with both central and peripheral functions; most of it is synthesized in the gut and stored in platelets in the circulatory pool. The prevalence of thrombocytopenia in these patients suggests a potential role for platelets in CAVD. Histological evidence confirms the presence of platelet aggregates on diseased valves, indicating that local particle release may promote disease progression. The valvular disease effects of excessive serotonin signaling through stimulation of the 5-HT2B receptor on the VIC are well-documented in drug-induced and carcinoid-associated valvular diseases. Paradoxically, studies have found decreased systemic serotonin levels in CAVD patients. This apparent paradox may be explained by increased local platelet activation and sequestration within the diseased valve, depleting the circulatory pool while simultaneously maintaining or even enhancing local bioactive concentrations. Random forest models identified indole-3-acrylic acid, a downstream metabolite of serotonin / serum metabolism, as a key discriminant, supporting the concept of altered metabolic fluxes through this pathway. Significant enrichment of the serotonin receptor signaling pathway in KEGG analysis further confirms the profound dysregulation of the serotonin system in CAVD. Therefore, serotonin appears to be a component of the perturbed metabolic network in CAVD, but its precise mechanistic role needs to be elucidated through direct studies of serotonin dynamics in valve tissue.

[0093] Several candidates have been identified in the exploration of biomarkers for CAVD. Lipid-related biomarkers include oxidized LDL, HDL, and Lp(a). Gut microbiota-derived metabolites such as TMAO and immunometabolic biomarkers such as FBP1 have also been shown to be associated with it. Inflammatory cytokines (such as RANTES) and non-coding RNAs (such as miR-17-5p) from activated monocytes have shown diagnostic potential, as have emerging hemodynamic alteration assessment methods.

[0094] This study has significant advantages, including its multi-stage design, which combines unbiased discovery with validation in large independent cohorts, and the complementary use of traditional statistics and machine learning. Extensive adjustments and consistent subgroup findings enhance the reliability of the invention's conclusions.

[0095] It should be noted that this invention measures serotonin levels in the system, rather than in valve tissue.

[0096] In summary, this metabolomics study identified serotonin as a key metabolite that is significantly downregulated in the systemic circulation of CAVD patients. The decrease in serotonin levels was consistent and independent of the severity of valvular stenosis. Furthermore, lower serotonin concentrations served as an independent predictor of CAVD and demonstrated good diagnostic accuracy. These findings position circulating serotonin as a promising novel biomarker for CAVD. Future large-scale prospective studies are necessary to validate its clinical value in early detection and improving risk stratification for this common valvular heart disease.

[0097] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. The biomarker or the substance that detects the biomarker in one or more of the following applications: a1) Prepare products for the diagnosis or auxiliary diagnosis of calcific aortic valve disease; a2) Prepare products for the prevention and / or treatment of calcific aortic valve disease; a3) Prepare products for screening calcific aortic valve disease; a4) To prepare products for predicting or assessing the risk of calcific aortic valve disease; a5) Prepare products for prognostic assessment of calcific aortic valve disease; The biomarker is serotonin.

2. The application according to claim 1, characterized in that, The substance used to detect biomarkers is a reagent for detecting serotonin levels.

3. The application according to claim 1, characterized in that, The products mentioned include chips, reagents, test strips, drugs, formulations, reagent kits, high-throughput screening platforms, or biosensors.

4. A product characterized in that, The product includes a substance for detecting serotonin; the product has any one or more of the following uses: a1) Prepare products for the diagnosis or auxiliary diagnosis of calcific aortic valve disease; a2) Prepare products for the prevention and / or treatment of calcific aortic valve disease; a3) Prepare products for screening calcific aortic valve disease; a4) To prepare products for predicting or assessing the risk of calcific aortic valve disease; a5) Prepare products for prognostic assessment of calcific aortic valve disease.

5. The product according to claim 4, characterized in that, The products include chips, reagents, test strips, drugs, formulations, reagent kits, high-throughput screening platforms, or biosensors.

6. A method for screening drugs for calcified aortic valve disease, characterized in that, include: c1) The system expressing and / or containing the serotonin is treated with the candidate substance; Set up parallel controls that do not use candidate substance treatment; c2) After completing step c1), detect the serotonin content in the system; if the serotonin content in the system treated with the candidate substance is significantly increased compared with the parallel control, the candidate substance can be used as a candidate drug for calcified aortic valve disease.

7. The application of substances that increase serotonin levels in product preparation, characterized in that, The product's functions include the prevention and / or treatment of calcific aortic valve disease.

8. A device for detecting, diagnosing, assisting in the diagnosis, screening, risk prediction, or prognostic assessment of calcific aortic valve disease, characterized in that, The device includes reagents for detecting serotonin levels and a computer-readable storage medium storing a computer program that causes the computer to perform the following steps: Calcification of aortic valve disease can be detected, diagnosed, aided in diagnosis, screened, risk predicted, or prognostic assessed based on serotonin levels.

9. A system for prognostic assessment or auxiliary prognostic assessment of calcific aortic valve disease, characterized in that, The system includes: b1) An analysis unit, the analysis unit comprising: a detection substance selected from serotonin content in a test sample of a subject; b2) An assessment unit comprising: performing a prognostic assessment of the subject based on the serotonin level determined in b1).

10. A product characterized in that, Its active ingredients include substances for increasing serotonin levels, and the product functions to prevent and / or treat calcified aortic valve disease.

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