COPD-PH specific metabolic marker combination screened based on metabonomics and machine learning

Abstract

The invention discloses a chronic obstructive pulmonary disease related pulmonary arterial hypertension (COPD-PH) specific metabolic marker combination screened based on metabonomics and machine learning, and relates to the technical field of biological medicines. According to the invention, the specific metabolic characteristics of COPD-PH patients are found for the first time. The method comprises the following steps: firstly, performing non-targeted metabonomics analysis on plasma samples of COPD-PH patients, COPD patients and healthy controls to identify 163 differential metabolites, and then comprehensively analyzing through three machine learning algorithms of RF, SVM and LASSO to screen five key metabolites: ornithine, histidine, biotin, nicotinamide and homocystine. In the multi-center verification research, the combination of the five metabolites also shows excellent diagnostic efficiency [AUC = 0.814], which is obviously superior to the traditional diagnostic index. The invention not only provides a new tool for noninvasive diagnosis of COPD-PH, but also deeply reveals the metabolic mechanism of occurrence and development of diseases.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and in particular relates to a COPD-PH-specific metabolite marker combination and application thereof based on metabolomics and machine learning screening. Background Art

[0002] Chronic obstructive pulmonary disease-related pulmonary hypertension (COPD-PH) is a serious complication of COPD progression, but its diagnosis faces significant challenges. Current clinical practice has the following key issues:

[0003] Symptom overlap and high misdiagnosis rate: The clinical manifestations of COPD and PH (such as dyspnea and decreased exercise tolerance) are highly similar, resulting in delayed diagnosis or missed diagnosis rate exceeding 30% (Braganza M, Shaw J, Solverson K, Vis D, Janovcik J, Varughese RA, Thakrar MV, Hirani N, Helmersen D, Weatherald J: A Prospective Evaluation of the Diagnostic Accuracy of the Physical Examination for Pulmonary Hypertension. Chest 2019, 155(5): 982-990.; Dauriat G, Reynaud-Gaubert M, Cottin V, Lamia B, Montani D, Canuet M, Boissin C, Tromeur C, Chaouat A, Degano Bet al: Severe pulmonary hypertension associated with chronic obstructive pulmonary disease: A prospective French multicenter cohort. The Journal of heart and lung transplantation: the official publication of the International Society for Heart Transplantation 2021, 40(9): 1009-1018.). The current diagnosis relies on the "suspect-support-confirm-stratify" strategy recommended by the 6th World Symposium on Pulmonary Hypertension (WSPH), but this process is complex and requires the combination of multimodal examinations such as chest CT, echocardiography, and pulmonary function tests (Galiè N, McLaughlin VV, Rubin LJ, Simonneau G: An overview of the 6th World Symposium on Pulmonary Hypertension. Eur Respir J 2019, 53(1).).

[0004] Limitations of exclusive diagnosis: In the context of chronic lung disease, the etiology of pulmonary hypertension is often complex and may coexist with other risk factors such as HIV infection, connective tissue disease, portal hypertension or chronic thrombotic pulmonary hypertension. This makes COPD-PH still mainly diagnosed by exclusive diagnosis. This makes it difficult for primary medical institutions to complete the task, and patients often need to be referred to specialist centers (Kerr KM, Elliott CG, Chin K, Benza RL, Channick RN, Davis RD, He F, LaCroix A, Madani MM, McLaughlin VV et al: Results From the United States Chronic Thromboembolic Pulmonary Hypertension Registry: EnrollmentCharacteristics and 1-Year Follow-up. Chest 2021, 160(5): 1822-1831.; Humbert M, Kovacs G, Hoeper MM, Badagliacca R, Berger RMF, Brida M, Carlsen J, Coats AJS, Escribano-Subias P, Ferrari P et al: 2022ESC / ERS Guidelines for the diagnosis and treatment of pulmonary hypertension. Eur Respir J 2023, 61(1).).

[0005] Invasive limitations of the gold standard: Although right cardiac catheterization (RHC) is the gold standard for diagnosis, it is not suitable for routine screening of COPD patients due to its high operational risks and strict technical threshold. In addition, existing imaging markers (such as right ventricular diameter / left ventricular diameter ratio and pulmonary artery stiffness parameters) have potential but rely on expensive equipment (such as cardiovascular magnetic resonance imaging) and lack standardization (Charters PFP, Rossdale J, Brown W, Burnett TA, Komber H, Thompson C, Robinson G, MacKenzie Ross R, Suntharalingam J, Rodrigues JCL: Diagnostic accuracy of an automated artificial intelligence derived right ventricular to left ventricular diameter ratio tool on CT pulmonary angiography to predict pulmonary hypertension at right heart catheterization. Clinical radiology 2022, 77(7):e500-e508.; Agoston-Coldea L, Lupu S, Mocan T: Pulmonary Artery Stiffness by Cardiac Magnetic Resonance Imaging Predicts Major Adverse Cardiovascular Events in patients with Chronic Obstructive Pulmonary Disease. SciRep 2018,8(1):14447.). The existing diagnostic process is complex and time-consuming. The current diagnosis of COPD-PH requires a combination of multiple clinical symptoms and examination results. This complex evaluation process not only increases medical costs but may also lead to delayed diagnosis and missed treatment opportunities.

[0006] Limitations of existing technologies: Single markers and low specificity: Previous studies have focused on single metabolites (such as B-type natriuretic peptide), but the heterogeneity of COPD-PH requires a multi-marker assessment. Lack of noninvasive dynamic monitoring tools: Imaging or hemodynamic studies cannot meet the needs of early disease screening and long-term follow-up.

[0007] Research gaps in metabolic disorders and pulmonary vascular remodeling: Although the role of metabolic abnormalities in arterial pulmonary hypertension (PAH) has been widely studied, there is currently almost no research on the metabolic characteristics of COPD-PH, and there are few reports on studies on COPD-PH-specific metabolic markers. Summary of the Invention

[0008] In response to the above-mentioned deficiencies in the prior art, the present invention discovered for the first time the specific metabolic characteristics of COPD-PH patients through systematic metabolomics research. Plasma samples from 85 COPD-PH patients, 99 COPD patients and 189 healthy controls were first collected for non-targeted metabolomics analysis, and 163 differential metabolites were identified. Then, through comprehensive analysis using three machine learning algorithms, RF, SVM and LASSO, five key metabolites were finally screened out: ornithine, histidine, biotin, nicotinamide and homocysteine. At the same time, in a multicenter validation study (116 new samples), the combination of these five metabolites also showed excellent diagnostic efficacy (AUC = 0.814), which was significantly better than traditional diagnostic indicators. This discovery not only provides a new tool for the non-invasive diagnosis of COPD-PH, but also reveals in depth the metabolic mechanism of the occurrence and development of the disease.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] One of the objects of the present invention is to provide a metabolic marker combination, which includes ornithine, histidine, biotin, nicotinamide and homocysteine.

[0011] A second object of the present invention is to provide the use of the metabolic marker combination in the preparation of COPD-PH disease diagnosis products, early screening products, disease monitoring products and / or drug efficacy monitoring products.

[0012] A third object of the present invention is to provide a screening method for the metabolite marker combination, which includes two stages: a retrospective modeling stage and a prospective validation stage; the retrospective modeling stage constructs a prediction model based on the metabolite peak intensity characteristics; the prospective validation stage reconstructs a clinical practical model through targeted metabolomics quantitative data.

[0013] Furthermore, the research subjects in the retrospective modeling stage and the prospective validation stage both include a COPD-PH group, a COPD group, and a normal group.

[0014] Furthermore, obtaining the metabolite peak intensity characteristics in the retrospective modeling stage includes using non-targeted and targeted quantitative metabolomics analysis methods.

[0015] Furthermore, in the retrospective modeling stage, a multidimensional feature screening system was constructed using three machine learning algorithms: random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO). Then, after cross-validation, five candidate metabolites were obtained: ornithine, histidine, biotin, nicotinamide, and homocysteine.

[0016] A fourth object of the present invention is to provide a product for COPD-PH disease diagnosis, early screening, disease monitoring and / or drug efficacy monitoring, wherein the product comprises a reagent for detecting the metabolic marker combination.

[0017] Furthermore, the product form includes a kit.

[0018] A fifth object of the present invention is to provide a model for COPD-PH disease diagnosis, early screening, disease monitoring and / or drug efficacy monitoring, wherein the method for constructing the model includes using the metabolic marker combination.

[0019] A sixth object of the present invention is to provide a system for COPD-PH disease diagnosis, early screening, disease condition monitoring and / or drug efficacy monitoring, wherein the system includes a module using the metabolic marker combination.

[0020] Compared with the prior art, the present invention has the following technical effects:

[0021] 1. Beneficial effects

[0022] 1. Improved diagnostic efficiency

[0023] High precision: The five metabolic markers (ornithine, histidine, biotin, nicotinamide, and homocysteine) screened by the multi-model machine learning algorithm had a combined diagnostic AUC of 0.879 (sensitivity 72.9%, specificity 91.9%) in a retrospective cohort, and the prospective validation AUC remained at 0.814 (sensitivity 60.0%, specificity 83.9%), significantly outperforming traditional echocardiography or clinical scores (such as the B-type natriuretic peptide test recommended by the ESC / ERS guidelines).

[0024] Stability verification: A double-cohort design (retrospective n=85 COPD-PH / 99 COPD / 189 healthy; prospective n=50 COPD-PH / 35 COPD / 31 healthy) was used to verify the model's cross-center applicability and reduce single-center bias.

[0025] 2. Methodological optimization

[0026] Multi-algorithm joint screening: Integrates three machine learning algorithms, RF, SVM, and LASSO, to avoid overfitting of a single model.

[0027] Metabolomics technology innovation: combining non-targeted metabolomics [high-resolution liquid chromatography-mass spectrometry (LC-MS / MS)] global screening with targeted metabolomics (MRM mode) precise quantification, with a detection sensitivity of 0.1 ng / mL (R 2 >0.99), intra-assay / inter-assay CV<10%, meeting clinical testing standards.

[0028] A two-stage, cross-cohort validation system: The collaborative design of a retrospective cohort (n=373) and a prospective multicenter cohort (n=116), combining the complementary techniques of untargeted metabolomics (global screening) and targeted metabolomics (precise quantification), ensures the universality of the study conclusions and their potential for clinical translation. The prospective validation phase employed dual screening criteria (trend consistency test + Kruskal-Wallis multiple comparisons) to identify five core biomarkers (ornithine, histidine, biotin, nicotinamide, and homocysteine). Their biological stability was fully demonstrated through cross-platform (LC-MS vs. MRM) and cross-cohort validation.

[0029] 3. Clinical practicality

[0030] This invention addresses the clinical pain point of the lack of non-invasive diagnostic methods for COPD-PH and proposes a metabolomics non-invasive screening system for the first time, breaking through the limitations of existing technologies that rely on invasive examinations. Its core value and innovative application scenarios are as follows:

[0031] Simplified testing process: The risk scoring formula only requires the concentrations of five metabolites (ornithine, histidine, etc.), which is easy to calculate and the test items are compatible with conventional clinical laboratory platforms. Currently, the clinical diagnosis of COPD-PH relies on right cardiac catheterization (invasive, expensive, and difficult to popularize in grassroots hospitals). However, this invention only requires blood testing combined with conventional ultrasound to complete high-risk population screening, and is particularly suitable for:

[0032] Early community screening: In medical institutions that lack right cardiac catheter equipment, the combination of "ultrasound + metabolic panel" can be used to achieve initial screening of high-risk groups and reduce the missed diagnosis rate.

[0033] Efficacy monitoring: Histidine (reflecting inflammatory status) and homocysteine ​​(a marker of oxidative stress) in the model can serve as biomarkers of responsiveness to drugs (such as phosphodiesterase-5 inhibitors).

[0034] Filling a diagnostic gap: Current WHO guidelines lack specific non-invasive methods for the diagnosis of COPD-PH. Echocardiographic assessment of pulmonary artery systolic pressure (PASP) in patients with emphysema has an error rate of up to 30%. This invention addresses the limitations of imaging through metabolomics characterization.

[0035] 2. Innovation

[0036] 1. Scientific discovery and innovation

[0037] Metabolic pathway mechanism: The core role of the urea cycle-nicotinamide metabolic axis in COPD-PH was revealed for the first time:

[0038] Abnormalities in ornithine (urea cycle) lead to accumulation of blood ammonia, driving pulmonary endothelial damage;

[0039] Nicotinamide (NAD+ metabolism) depletion exacerbates mitochondrial dysfunction and promotes pulmonary artery smooth muscle proliferation.

[0040] Biomarker synergy: Discovered the synergistic diagnostic value of homocysteine ​​(oxidative stress) and histidine (inflammatory regulation).

[0041] 2. Innovation in technical methods

[0042] Two-stage modeling strategy:

[0043] Retrospective phase: constructing a high-sensitivity model based on peak intensity (for research purposes);

[0044] Prospective stage: Convert categorical variables to establish clinically applicable models to solve the bottleneck of clinical translation of metabolomics data.

[0045] Dynamic threshold design: Set personalized cutoff values ​​for different metabolites (such as ornithine 34,000, homocysteine ​​350) to optimize diagnostic specificity.

[0046] 3. Research design innovation

[0047] Multicenter cohort validation: The prospective cohort includes the First Affiliated Hospital of Guangzhou Medical University and Shanghai Pulmonary Hospital, covering populations from both northern and southern China, enhancing the universality of the results.

[0048] 3. Clinical Application Value

[0049] 1. Early diagnosis

[0050] Non-invasive alternatives: Compared with right heart catheterization (gold standard), plasma metabolic marker testing can achieve non-invasive, low-cost (cost reduced by about 60%) screening, which is suitable for promotion in primary hospitals.

[0051] Stratification of high-risk groups: Conduct metabolic screening on COPD patients to provide early warning of PH risk.

[0052] 2. Precision treatment guidance

[0053] Targeted intervention targets: Model markers point to intervention pathways (such as nicotinamide supplementation to improve NAD+ levels and regulation of the urea cycle to reduce ammonia toxicity), providing a basis for individualized treatment.

[0054] Efficacy monitoring: Dynamically monitor changes in the ornithine / homocysteine ​​ratio and evaluate the response rate to vasodilators (such as phosphodiesterase 5 inhibitors) (preliminary data showed AUC 0.72).

[0055] 3. Health economic benefits

[0056] Reduce unnecessary right heart catheterization: The model has a negative predictive value of 89%, which can avoid approximately 40% of invasive examinations.

[0057] Optimize medical insurance expenditure: the cost per test is significantly lower than right heart catheterization.

[0058] IV. Conclusion

[0059] This study established the first non-invasive metabolomics-based diagnostic system for COPD-PH through multi-model machine learning algorithm screening, dual-cohort clinical validation, and in-depth analysis of metabolic mechanisms. Its innovations are reflected in:

[0060] Methodology: Multiple algorithms are combined to improve the efficiency of marker screening;

[0061] Scientific discovery: Revealing the core role of the urea cycle-energy metabolism axis;

[0062] Clinical translation: Provide simple, low-cost, and scalable detection solutions.

[0063] In the future, multicenter randomized controlled trials can be used to further verify its value in improving clinical decision-making.

[0064] In summary, this invention systematically describes for the first time the metabolic characteristics of COPD-PH; establishes an efficient noninvasive diagnostic model; identifies new potential therapeutic targets; and addresses the invasive, expensive, and complex nature of existing technologies. The developed five-marker panel offers advantages such as high sample stability and low testing costs, providing a scalable screening tool for primary care institutions. This not only fills a gap in the field of COPD-PH metabolic diagnosis but also opens new avenues for early diagnosis and precise treatment of COPD-PH, with significant clinical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 The metabolic difference analysis process and results of COPD-PH, COPD and normal groups in Example 1 of the present invention are shown;

[0066] Figure 2 The process of constructing a multidimensional feature screening system for three machine learning algorithms with complementary features in Example 1 of the present invention;

[0067] Figure 3 The peak expression characteristics of the five metabolites in Example 1 of the present invention in the retrospective cohort and the quantitative detection results in the prospective cohort;

[0068] Figure 4 The diagnostic performance of the prediction model in the retrospective modeling phase and the predictive efficacy of the clinical practical model in the prospective validation phase in Example 1 of the present invention;

[0069] Figure 5 These are the ROC analysis results of each metabolite individually predicting COPD-PH in the retrospective cohort and prospective cohort of Example 1 of the present invention. DETAILED DESCRIPTION

[0070] The following examples are intended to illustrate the present invention but are not intended to limit the scope of the invention. Without departing from the spirit and essence of the present invention, modifications or substitutions made to the inventive method, steps or conditions are intended to fall within the scope of the present invention. The reagents, products and instruments used in the following examples are all commercially available, and the methods used in the examples are consistent with conventional methods unless otherwise specified.

[0071] The technical solution of the present invention is further elaborated in detail below in conjunction with embodiments.

[0072] Example 1

[0073] 1 Retrospective cohort study subjects

[0074] 1.1COPD-PH group:

[0075] A total of 250 patients with COPD and pulmonary hypertension (PH) diagnosed by right cardiac catheterization were enrolled at the First Affiliated Hospital of Guangzhou Medical University between December 2008 and December 2024. The inclusion criteria for the COPD-PH group included: (I) age ≥18 years; (II) fulfillment of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) pulmonary function criteria (i.e., forced expiratory volume in 1 second to forced vital capacity ratio after bronchodilator testing (FEV1 / FVC) <70%), along with corresponding clinical manifestations and medical history; (III) signs of pulmonary parenchymal disease on chest radiograph or chest CT; (IV) hemodynamic diagnosis of PH was based on mean pulmonary artery pressure (mPAP) >20 mmHg, pulmonary artery wedge pressure (PAWP) ≤15 mmHg, and pulmonary vascular resistance (PVR) ≥2 Wood units; and (V) patients met the COPD-related subtype of WHO group 3 PH (group 3.1).

[0076] Exclusion criteria were: (I) lack of right heart catheterization data; (II) concomitant congenital heart disease or left heart system disease; (III) history of lung diseases such as pulmonary embolism, interstitial lung disease, pulmonary fibrosis with emphysema, active pulmonary tuberculosis, or severe bronchiectasis; (IV) history of renal insufficiency, hematologic diseases, rheumatic autoimmune diseases, immunodeficiency, hyperthyroidism, connective tissue diseases (such as systemic sclerosis / scleroderma, systemic lupus erythematosus, etc.), sarcoidosis, human immunodeficiency virus infection, mediastinal fibrosis, and other WHO class 1, 2, 4, or 5 pulmonary hypertension-related diseases; and (IV) malignant tumors.

[0077] 1.2COPD group:

[0078] A total of 357 patients with COPD and a pulmonary artery systolic pressure (PASP) <35 mmHg on echocardiography or mPAP <20 mmHg on right cardiac catheterization were enrolled at the First Affiliated Hospital of Guangzhou Medical University between October 2009 and September 2023. Inclusion criteria for the COPD group included: (I) age ≥18 years; (II) meeting the GOLD pulmonary function test criteria (FEV1 / FVC <70%) and having corresponding clinical manifestations and medical history; (III) chest radiograph or chest CT showing signs of pulmonary parenchymal disease; (IV) hemodynamic diagnosis of pulmonary hypertension with mPAP <20 mmHg; or echocardiographic pulmonary artery systolic pressure (PASP) <35 mmHg or absence of tricuspid regurgitation.

[0079] Exclusion criteria were: (I) severe cardiovascular disease; (II) history of lung diseases such as pulmonary embolism, interstitial lung disease, pulmonary fibrosis with emphysema, active pulmonary tuberculosis, or severe bronchiectasis; (III) acute lung infection within 4 weeks; (IV) history of renal insufficiency, hematologic diseases, rheumatic autoimmune diseases, immunodeficiency, hyperthyroidism, connective tissue diseases (such as systemic sclerosis / scleroderma, systemic lupus erythematosus, etc.), sarcoidosis, human immunodeficiency virus infection, mediastinal fibrosis, and (IV) malignant tumors.

[0080] 1.3 Normal group: 189 healthy subjects matched with gender and age were recruited.

[0081] The Medical Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University has approved the clinical research plan (Medical Research Ethics Review 2021 No. 09).

[0082] 2. Prospective cohort study subjects

[0083] Between October 2023 and November 2024, 50 COPD patients with PH confirmed by right cardiac catheterization were enrolled at the First Affiliated Hospital of Guangzhou Medical University and Shanghai Pulmonary Hospital. Between July and November 2024, 35 COPD patients with echocardiographic PASP <35 ​​mmHg or right cardiac catheterization mPAP <20 mmHg were enrolled. Between September and November 2024, 31 sex- and age-matched healthy subjects were recruited. Inclusion and exclusion criteria for each group remained consistent with those of a previous retrospective cohort study.

[0084] The Medical Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University has approved the clinical research plan (Ethics Number: ES-2024-043-01).

[0085] 3. Non-targeted and targeted metabolomics

[0086] Untargeted metabolomics uses LC-MS / MS to perform a comprehensive and unbiased screening of small molecule metabolites in biological plasma samples. First, proteins are removed and metabolites are extracted by methanol or acetonitrile precipitation. Separation is performed on a reversed-phase column (such as Waters ACQUITY UPLC HSS T3, 1.8 μm, 2.1×100 mm). The mobile phases are water containing 0.1% formic acid (phase A) and acetonitrile (phase B). The gradient elution program (such as 2-95% phase B, 20 minutes) is combined with full scan (m / z 50-1500 Da) in positive and negative ion modes of electrospray ionization (ESI) and data-dependent acquisition (DDA) to obtain metabolite mass spectrometry information. After peak extraction, alignment, and normalization using software such as Progenesis QI, quality control (QC) samples are prepared by mixing the same amount of plasma from each sample and extracting metabolites using the same procedure as the test samples. Differential metabolites (VIP ≥ 1 and P < 0.05) were screened by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), and metabolite annotation was performed based on databases such as HMDB. MetaboAnalyst (http: / / www.metaboanalyst.ca / ) was used for metabolic pathway enrichment analysis to elucidate the potential pathological mechanism. Targeted metabolomics uses multiple reaction monitoring (MRM) mode for precise quantification of key metabolites screened out by non-targeted screening. By optimizing chromatographic conditions (such as retention time of specific metabolites) and mass spectrometry parameters (such as collision energy), isotope internal standard calibration was used to establish a standard curve (0.1-1000 ng / mL, R 2>0.99), and validated methodological parameters (recovery 85-115%, intra-assay / inter-assay CV <10%). The two approaches complement each other: non-targeted metabolomics provides a global metabolic profile, while targeted metabolomics ensures quantitative accuracy and clinical translational value.

[0087] 4. Statistical Analysis

[0088] Continuous variables: Data that conformed to normal distribution were expressed as mean ± standard deviation (mean ± SD), and inter-group comparisons were performed using one-way analysis of variance (ANOVA) or independent sample t-test; non-normally distributed data were expressed as median (interquartile range) [M (IQR)], and inter-group comparisons were performed using Mann-Whitney U test (two groups) or Kruskal-Wallis test (multiple groups). Categorical variables: Frequency (percentage) [n (%)] was expressed, and inter-group comparisons were performed using chi-square test (χ 2 ) or Fisher's exact test (when expected frequency < 5). Metabolite expression levels among the three groups were assessed by Kruskal-Wallis test. Area under the curve (AUC) analysis was performed using the pROC software package (version 1.16.2). Spearman correlation analysis was used to determine the correlation coefficients between the clinical indicators and the main differentially expressed metabolites. All statistical analyses were performed using SPSS statistical software (version 26.0; IBM, Armonk, NY, USA) and R software (version 4.2.2; R Foundation for Statistical Computing, Vienna, Austria). P < 0.05 was considered statistically significant.

[0089] 5. Results

[0090] 5.1 Plasma metabolic landscapes of COPD-PH, COPD, and normal groups

[0091] To obtain a comprehensive metabolomic landscape from normal controls to COPD and COPD-PH, we retrospectively collected plasma samples from 85 COPD-PH patients, 99 COPD patients, and 189 sex- and age-matched healthy controls for large-scale LC-MS-based metabolomic analysis. Furthermore, we prospectively collected another independent cohort (n = 50 for COPD-PH, n = 35 for COPD, and n = 31 for normal controls) for targeted metabolomic analysis.

[0092] We detected 1619 metabolites in plasma samples, covering a wide range of biochemical components, including amino acids, organic acids, bile acids, nucleotides, acylcarnitines, coenzymes and vitamins, carbohydrates, purines and pyrimidines, and other metabolites. PCA showed that the metabolic signatures were different between COPD-PH, COPD, and normal group samples ( Figure 1 a). Partial PLS-DA clearly distinguished the metabolic characteristics of COPD-PH, COPD and normal groups ( Figure 1 b) The partial overlap in metabolic signatures between COPD-PH and COPD observed in PCA and PLS-DA analyses strongly suggests a gradual metabolic evolution between these two disease states. This continuous, but incomplete, pattern of separation reflects that the progression from pure COPD to COPD-PH is not a sudden pathological shift, but rather a dynamic process of gradual remodeling of the metabolic network. The overlapping regions may represent: 1) a high-risk subgroup of COPD patients with pre-PH metabolic features; 2) heterogeneity in the rate of disease progression across individuals; and 3) pathophysiological turning points triggered by threshold concentrations of certain metabolites.

[0093] 5.2 Dynamic Evolution of Systemic Metabolic Reprogramming in the Progression of COPD to COPD-PH

[0094] 5.2.1 Comparison of metabolic characteristics between healthy controls and COPD-PH group

[0095] Metabolic reprogramming is a core mechanism in the development and progression of COPD-PH. PCA revealed a significant separation of plasma metabolomic profiles between COPD-PH patients and healthy controls, with the two groups forming independent clusters in the dimensionality-reduced space. PLS-DA further identified the core metabolic signatures driving this differentiation. Differential metabolite analysis (Wilcoxon signed-rank test, P < 0.05, FC > 1.2 or < 0.83) identified 646 significantly altered metabolites, of which characteristic metabolites such as ethylenediamine-N,N'-diacetic acid, aspartate-serine complex, and ADP-glucose exhibited the most significant abundance changes. Functional enrichment analysis showed that these differential metabolites were mainly involved in metabolic pathways such as purine metabolism, Warburg effect, and various arginine and proline metabolism, and urea cycle. Pathway topology analysis further confirmed the significant activation of the alanine-aspartate-glutamate metabolic axis and the pentose phosphate pathway, suggesting that the synergistic effect of abnormal energy metabolism (such as enhanced glycolysis and inhibited oxidative phosphorylation) and oxidative stress (urea cycle disorder) may drive pulmonary vascular remodeling and the progression of pulmonary hypertension.

[0096] 5.2.2 Metabolic differences between healthy controls and COPD patients

[0097] During the progression of COPD, metabolic perturbations become prominent, and COPD patients also exhibit metabolic profiles that are significantly different from healthy controls. PCA models revealed that the distribution of COPD samples in metabolic space deviated significantly from that of healthy controls, while PLS-DA models accurately identified key metabolic markers that distinguished the two groups. Differential analysis identified 634 significantly altered metabolites, including signature molecules such as N5-modified ornithine (possibly associated with nitric oxide metabolism) and acetylcarnitine (a marker of mitochondrial fatty acid β-oxidation). Notably, these metabolic changes were primarily enriched in the arginine-proline metabolic network and the pentose phosphate pathway, presenting a pattern of metabolic disturbance that partially overlaps with that of the COPD-PH group, but to a lesser degree. Disturbances in the arginine / proline metabolic pathway and urea cycle abnormalities suggest that impaired nitric oxide synthesis may exacerbate airway remodeling. Furthermore, abnormalities in nicotinamide metabolism and phosphatidylcholine biosynthesis may reflect compensatory regulation of mitochondrial function and impaired cell membrane stability, providing potential targets for early intervention.

[0098] 5.2.3 Metabolic Progression Characteristics of COPD and COPD-PH Groups

[0099] Progression from COPD to COPD-PH is accompanied by a significant expansion of metabolic dysregulation. PCA and PLS-DA revealed that while the metabolic profiles of COPD-PH and COPD groups partially overlapped, there was a clear demarcation, indicating that disease progression is accompanied by both continuous changes and specific exacerbations in metabolic status. A total of 499 differential metabolites were statistically different in COPD-PH compared with COPD. Abnormalities in inosine-5'-tetraphosphate and cysteine-glycine complexes suggest an imbalance in purine metabolism and a breakdown in redox homeostasis. Pathway analysis revealed that these metabolic changes primarily involved the urea cycle and nicotinamide metabolism, reflecting the pathological hallmarks of disturbed ammonia metabolism and exacerbated energy crisis during disease progression.

[0100] 5.2.4 Intersection of three groups of shared differential metabolites reveals core metabolic pathway dysregulation

[0101] We intersected the differential metabolites between the healthy control and COPD-PH group, the healthy control and COPD group, and the COPD and COPD-PH group to find the common differential metabolites among the three groups, and found a total of 163 ( Figure 1c). This includes 19 organic acids and their derivatives, 11 amino acids, 11 amino acid derivatives, 6 nucleotides and their metabolites, 4 sugars, 4 sugar phosphates, 3 benzenes and their derivatives, 2 phenols, 2 coenzymes and vitamins, 2 alcohols and polyols, 2 purines and pyrimidines, 2 fatty acids, 2 sugar alcohols, 3 sugar acids, 2 indoles and their derivatives, 4 phosphate compounds, 1 pyridine and its derivatives, 1 imidazole, and 9 other categories. Enrichment analysis showed that the differential metabolites were significantly enriched in the pentose phosphate pathway, histidine metabolism, methylhistidine metabolism, glycine and serine metabolism, arginine and proline metabolism, urea cycle, methionine metabolism, and alanine metabolism ( Figure 1 d). Pathway enrichment analysis showed that metabolic pathways such as the pentose phosphate pathway, histidine metabolism, interconversion of pentose and glucuronic acid esters, arginine and proline metabolism, a folate carbon pool, glycine, serine and threonine metabolism, purine metabolism, B-alanine metabolism, amino sugar and nucleotide sugar metabolism were significantly enriched ( Figure 1 e).

[0102] The progression of COPD to COPD-PH demonstrates a dual pattern of metabolic disturbances characterized by both step-wise accumulation and qualitative deterioration. The number of differentially expressed metabolites increases with disease stage (634 metabolites from healthy to COPD, 646 metabolites from healthy to COPD-PH, and 499 metabolites from COPD to COPD-PH), suggesting irreversible accumulation of metabolic disorders. Throughout the disease progression from healthy to COPD, and then to COPD-PH, the pentose phosphate pathway, arginine / proline metabolism, and urea cycle are continuously disrupted, manifesting as increased oxidative stress (increased NADPH demand), impaired nitric oxide synthesis, and the gradual accumulation of ammonia toxicity. These pathways are already significantly perturbed in the COPD stage, but upon progression to COPD-PH, ammonia metabolism collapses and the energy crisis worsens exponentially: complete urea cycle dysfunction leads to a surge in blood ammonia levels, abnormal nicotinamide metabolism (NAD+ depletion) exacerbates mitochondrial dysfunction, and the Warburg effect (dominance of aerobic glycolysis) and imbalances in purine metabolism (e.g., accumulation of inosine 5'-tetraphosphate) exacerbate ATP depletion. This vicious cycle drives oxidative damage to the lung vessels, smooth muscle proliferation, and irreversible remodeling, ultimately establishing the pathological progression of COPD-PH. Furthermore, the 163 differential metabolites shared by the three groups (such as aspartate-serine complexes and N5-ornithine) and their enriched pathways (such as histidine metabolism and the folate cycle) further confirm that the ammonia toxicity-energy metabolism axis is the core driving force of disease progression.

[0103] 5.3 Discovery and Dual-Cohort Validation of COPD-PH Metabolic Biomarkers Based on Multi-Model Machine Learning

[0104] The present invention adopts a multi-stage machine learning combined with metabolomics strategy to systematically screen and verify the key metabolite markers of COPD-PH. First, three machine learning algorithms with complementary characteristics, RF, SVM and LASSO ( Figure 2 A multi-dimensional feature screening system was constructed using a multi-model approach. After cross-validation, five candidate metabolites were identified: ornithine, histidine, biotin, nicotinamide, and homocysteine. This multi-model combined screening approach effectively reduced the selection bias of a single algorithm.

[0105] To enhance the clinical translational value of our findings, we designed a multicenter prospective validation cohort (n=50 in the COPD-PH group, n=35 in the COPD group, and n=31 in the healthy control group) and used targeted metabolomics to precisely quantify five metabolites. These metabolites play important regulatory roles in pathophysiological pathways such as purine metabolism and one-carbon unit metabolism, and may potentially be associated with the oxidative stress mechanisms of COPD-PH.

[0106] It is noteworthy that the peak expression characteristics of these five metabolites in the retrospective cohort ( Figure 3 ae) and quantitative detection results of prospective cohort ( Figure 3 fj) showed significant biological consistency. In the retrospective cohort, the AUC for individual metabolite prediction ranged from 0.709 (ornithine) to 0.793 (homocystine) ( Figure 5 ae), among which homocysteine ​​showed the best discriminatory efficacy. Targeted quantitative analysis of the prospective cohort further determined the optimal diagnostic cutoff value for each metabolite (ornithine: 34,000; histidine: 2,600; biotin: 430; nicotinamide: 92; homocysteine: 350; Figure S5). Notably, when these continuous variables were converted to categorical variables, their diagnostic efficacy (AUC 0.562-0.686, Figure 5 Although fj) decreased, it still maintained statistical significance, suggesting that changes in the absolute concentrations of metabolites may have more direct clinical significance in disease diagnosis.

[0107] The present invention adopts a two-stage study design: in the retrospective modeling stage, the prediction model constructed based on the metabolite peak intensity characteristics showed excellent diagnostic performance (AUC = 0.879, sensitivity 72.9%, specificity 91.9%) ( Figure 4 a); In the prospective validation phase, the clinical utility model reconstructed using targeted metabolomics quantitative data (risk score = 0.581 × ornithine + 1.252 × histidine + 1.299 × homocysteine ​​- 0.651 × nicotinamide + 1.414 × biotin - 1.176) maintained robust predictive efficacy in the prospective cohort (AUC = 0.814, sensitivity 60.0%, specificity 83.9%) ( Figure 4 b). It is particularly worth emphasizing that the metabolites included in the model are involved in multiple key pathophysiological processes, including the urea cycle (ornithine), inflammation regulation (histidine), energy metabolism (nicotinamide), and oxidative stress (homocystine). This provides a new research direction for a deeper understanding of the molecular mechanisms of COPD-PH.

[0108] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A metabolic marker combination, characterized in that: The metabolic marker combination includes ornithine, histidine, biotin, nicotinamide and homocysteine.

2. Use of the metabolic marker combination according to claim 1 in the preparation of COPD-PH disease diagnosis products, early screening products, disease monitoring products and / or drug efficacy monitoring products.

3. A method for screening the combination of metabolite markers according to claim 1, characterized in that: The screening method includes two stages: a retrospective modeling stage and a prospective validation stage; the retrospective modeling stage constructs a prediction model based on the metabolite peak intensity characteristics; The prospective validation phase reconstructs a clinically useful model using targeted metabolomics quantitative data.

4. The screening method according to claim 3, wherein The research subjects in the retrospective modeling phase and the prospective validation phase both include a COPD-PH group, a COPD group, and a normal group.

5. The screening method according to claim 4, characterized in that The acquisition of metabolite peak intensity characteristics in the retrospective modeling stage includes the use of non-targeted and targeted quantitative metabolomics analysis methods.

6. The screening method according to claim 5, characterized in that In the retrospective modeling stage, a multidimensional feature screening system was first constructed using three machine learning algorithms: random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO). Then, after cross-validation, five candidate metabolites were obtained: ornithine, histidine, biotin, nicotinamide, and homocysteine.

7. A product for COPD-PH disease diagnosis, early screening, disease monitoring and / or drug efficacy monitoring, characterized in that: The product comprises a reagent for detecting the metabolic marker combination according to claim 1.

8. The product according to claim 7, characterized in that Such product forms include kits.

9. A model for COPD-PH disease diagnosis, early screening, disease monitoring and / or drug efficacy monitoring, characterized in that: The method for constructing the model comprises using the metabolic marker combination described in claim 1.

10. A system for COPD-PH disease diagnosis, early screening, disease condition monitoring and / or drug efficacy monitoring, characterized in that: The system comprises using a module containing the metabolic marker combination according to claim 1.

Images