Serum biomarker and use thereof

Abstract

The present application relates to a serum biomarker and its application, in particular, to the use of a serum biomarker and / or a serum biomarker detection reagent in obesity typing, obesity prognosis evaluation, obesity management, obesity precision treatment, corresponding products, systems and methods.

Description

Technical Field

[0001] This application belongs to the field of biomedical detection technology and its applications, and relates to the use of serum biomarkers and / or serum biomarker detection reagents in obesity typing, obesity prognostic assessment, obesity management, and precision obesity treatment, as well as related products and methods. More specifically, this application provides methods, systems, products, and uses of serum biomarkers (e.g., serum cellobiose) through techniques such as liquid chromatography-mass spectrometry for obesity typing, obesity prognostic assessment, obesity management, and precision obesity treatment. Background Technology

[0002] Obesity has become a global public health problem. According to the World Health Organization (WHO), the global obese population has exceeded 650 million and is projected to reach 1.12 billion by 2030. In China, the overweight and obesity rates among adults are as high as 34.3% and 16.4%, respectively, and are showing a continuous upward trend. Obesity is not only a significant risk factor for many metabolic diseases, but is also closely related to the development and progression of cardiovascular disease, type 2 diabetes, non-alcoholic fatty liver disease (NAFLD), and various malignant tumors.

[0003] The significant health risks posed by obesity are not evenly distributed among all obese individuals. Recent studies have shown that obese people exhibit significant metabolic phenotypic heterogeneity, which can be further subdivided into two subtypes: "metabolicly healthy obesity" (MHO) and "metabolicly unhealthy obesity" (MUO), with significant differences in prognosis between the two.

[0004] Individuals with "metabolic healthy obesity" (MHO) have a high body mass index (BMI) but normal insulin sensitivity and no metabolic disorders such as hypertension, dyslipidemia, or insulin resistance, and their risk of cardiovascular disease is relatively low. In contrast, individuals with "metabolic unhealthy obesity" (MUO) have multiple metabolic abnormalities and obvious characteristics of metabolic syndrome, such as abnormal blood sugar and blood lipids, visceral fat accumulation, and chronic inflammation. Their risk of developing type 2 diabetes and cardiovascular disease in the future is significantly increased.

[0005] Currently, there is no single gold standard for differentiating / classifying MHO and MUO in clinical practice; basal metabolic indicators remain the core basis for clinical classification. For example, according to the NCEP (National Cholesterol Education Program) criteria, MUO patients exhibit significantly higher systolic / diastolic blood pressure, fasting blood glucose, triglycerides (TG), and lower high-density lipoprotein (HDL) levels. In addition, there are reports of using biomarkers such as the TyG index (triglyceride-glucose index), uric acid levels, serum zonulin, C-reactive protein (CRP), lipopolysaccharide-binding protein (LBP), liver fat content, fibroblast growth factor 21 (FGF21), ITLN1 (Omentin-1), and gut microbiota to differentiate between MHO and MUO in obese individuals. Serum zonulin has been reported to be useful in distinguishing between MHO and MUO. However, zonulin is expressed not only in intestinal epithelial cells but also in various tissues such as the liver, kidneys, heart, and brain. This multi-organ expression characteristic means that serum levels are affected by various factors, resulting in a high false-positive rate and limiting its application value in clinical diagnosis. Furthermore, some reports have used C-reactive protein (CRP) to differentiate between MHO and MUO; however, CRP is a non-specific acute-phase reactive protein, and its elevation can be seen in acute infections (bacteria / viruses), trauma, surgery, autoimmune diseases, tumors, smoking, etc. In addition, although MHO patients have normal metabolic indicators, they already exhibit a low-grade inflammatory state, with CRP levels between those of normal weight and MUO. This makes it impossible for CRP to accurately distinguish between the "relatively healthy" MHO and the "pathological" MUO.

[0006] More importantly, MHO is not a stable state; approximately 30-50% of MHO individuals will transform into MUO within a few years. Current standards cannot capture this dynamic evolution. Some obese individuals may have "normal" blood pressure, blood lipids, and blood sugar, but already possess severe subclinical inflammation, non-alcoholic fatty liver disease (NAFLD), or visceral fat accumulation. According to current standards, they would be classified as MHO, but in reality, they are already at high risk. Differentiating the pathological mechanisms of obese individuals holds promise for improving the accuracy of differential diagnosis between MHO and MUO.

[0007] As mentioned above, existing biomarkers used for the identification and typing of MHO and MUO generally suffer from low specificity, limited stability, and complex detection procedures. Therefore, there is an urgent need to develop a biomarker (or combination of biomarkers) with high sensitivity, high specificity, and high stability for the identification / typing of individuals with MHO and MUO. This biomarker should be able to detect a single fasting serum sample, overcoming the bottlenecks of existing technologies and difficulties in the detection process, thereby improving the accuracy of differential diagnosis and further promoting its application in clinical practice. Summary of the Invention

[0008] In view of the lack of effective biomarkers (or biomarker combinations) to distinguish between MHO and MUO in existing technologies, and the problems of insufficient specificity, high false positive rate, complex detection methods, and interference from multiple factors in the detection, classification and diagnosis of MHO / MUO, this invention provides a novel biomarker (or biomarker combination) based on serum metabolomics research for the application of obesity classification, obesity prognosis assessment, obesity management and precision treatment of obesity.

[0009] By providing novel biomarkers (or combinations of biomarkers) innovatively in this invention, the present invention achieves at least the following beneficial effects: (1) High specificity, serum cellobiose is significantly elevated in MUO patients, which is significantly different from MHO patients, and can effectively distinguish the two obesity subtypes; (2) High stability, among which serum cellobiose is relatively stable in serum as a metabolomics marker and is not easily affected by short-term factors; (3) The method is simple and the test can be completed with a single blood draw, which is convenient for clinical application.

[0010] In one aspect, this application provides the use of serum biomarker detection reagents in the preparation of products for obesity classification, obesity prognosis assessment, obesity management, and precision obesity treatment, wherein the serum biomarker includes serum cellobiose.

[0011] In one aspect, this application provides a kit for obesity typing, obesity prognostic assessment, obesity management, or precision treatment of obesity, comprising (i) a detection reagent for detecting serum biomarkers, said serum biomarkers including serum cellobiose; (ii) optionally, standards, such as a series of serum biomarker standard solutions; and / or other detection reagents for obesity typing; and (iii) a specification describing a method for distinguishing metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) by cutoff values, and clinical management recommendations based on the typing.

[0012] In one aspect, this application provides a system for obesity classification, obesity prognostic assessment, obesity management, or precision treatment of obesity.

[0013] In one aspect, this application provides a method for in vitro detection of serum cellobiose for obesity classification, obesity prognosis assessment, obesity management and / or precision treatment of obesity.

[0014] In one aspect, this application provides a method for constructing an obesity subtyping model. Attached Figure Description

[0015] The present invention will be further described below with reference to the accompanying drawings. The following description is only for illustrating the embodiments of the present invention and is not intended to limit the scope of the present invention.

[0016] Figure 1 The non-target metabolomics OPLS-DA analysis plots for the MHO and MUO groups in the training set show the metabolomics differences between the two groups. Each point in the figure represents a sample, and different colors represent the MHO group (green) and the MUO group (red), respectively. The horizontal axis (T score [1]) represents the predicted principal component, explaining 5.6% (R²) of the total data variation. 2 X=0.056), capturing systemic metabolic differences associated with the grouping variable; the vertical axis (Orthogonal T score [1]) represents the first orthogonal principal component, explaining 5.3% of the variation, reflecting intragroup individual differences, technical batch effects, or other biological / non-biological confounding factors unrelated to classification. OPLS-DA analysis showed significant separation between the two groups (MHO vs. MUO) at the metabolomics level, suggesting that MHO and MUO have different metabolic characteristics.

[0017] Figure 2 The figure shows the difference in serum cellobiose levels between the MHO and MUO groups in the training set, illustrating the comparison between the serum cellobiose levels in the MHO group (n=33) and the MUO group (n=33). As shown in the figure, the serum cellobiose level in the MUO group was significantly higher than that in the MHO group (n=33). p < The result of 0.0001 indicates that serum cellobiose can serve as a biomarker to distinguish between MHO and MUO.

[0018] Figure 3 To validate the difference in serum cellobiose levels between the MHO and MUO groups, a comparison of serum cellobiose levels between the MHO group (n=14) and the MUO group (n=16) is presented. As shown in the figure, the serum cellobiose level in the MUO group was also significantly higher than that in the MHO group (n=16). p <0.05), indicating that serum cellobiose can serve as a biomarker to distinguish between MHO and MUO.

[0019] Figure 4 To construct receiver operating characteristic (ROC) curves and calculate the area under the curve (AUC) using binary clinical outcomes (MHO vs. MUO) and target metabolite concentration data from the training set subjects, the optimal cutoff value was determined using the Youden Index maximization principle. As shown in the figure, AUC = (95% confidence interval: 0.72–0.91), and the optimal cutoff value is 57.71 μmol / L. High sensitivity and specificity are achieved at this critical value.

[0020] Figure 5 To construct receiver operating characteristic (ROC) curves and calculate the area under the curve (AUC) using binary clinical outcomes (MHO vs. MUO) and target metabolite concentration data from the validation set subjects, the AUC was 0.79 (95% confidence interval: 0.64–0.96), which is highly consistent with the training set results, indicating that the diagnostic model has good stability and reproducibility.

[0021] Figure 6 shows the Western blotting results (A) and quantitative analysis scatter plot (B) of Min6 cells treated with cellobiose, illustrating the protein expression levels of senescence markers Lamin B1 and p53 after treatment with different concentrations of cellobiose (0, 40, 60, 80 μM). The results show that cellobiose dose-dependently downregulates Lamin B1 expression and upregulates p53 expression, suggesting that cellobiose can directly induce pancreatic β-cell senescence and demonstrating its potential as an effective marker for differentiating between MUO and MHO. Detailed Implementation

[0022] All numerical ranges provided herein are intended to clearly include all values ​​falling between the endpoints of the range and the range of values ​​between them. Features mentioned in the invention or embodiments may be combined. All features disclosed in this specification may be used in any combination form, and each feature disclosed in the specification may be replaced by any alternative feature that provides the same, equivalent, or similar purpose. Therefore, unless otherwise specified, the disclosed features are merely general examples of equivalent or similar features.

[0023] As used in this article, “containing,” “having,” or “including” includes “containing,” “mainly composed of,” “substantially composed of,” and “composed of”; “mainly composed of,” “substantially composed of,” and “composed of” are subordinate concepts of “containing,” “having,” or “including.”

[0024] The numerical ranges in this article include their endpoints and the specific numerical points and subranges within that range. For example, 1 to 3 includes endpoints 1 and 3, the specific integer numerical points 2 and non-integer numerical points (e.g., but not limited to: 1.2, 1.5, 1.8, 2.1, 2.3, 2.4, 2.8, etc.), and their subranges (e.g., but not limited to: 1 to 2, 2 to 3, 1 to 1.2, 1.5 to 1.8, etc.).

[0025] As used in this article, the term "cellobioose" refers to a disaccharide formed by two glucose molecules linked by a glycosidic bond, with the chemical formula C0. 12 H 22 O 11The molecular weight is 342.3 Da. Cellobiose exists in two isomers: α-cellobiose (4-O-β-D-glucopyranosyl-α-D-glucopyranose) and β-cellobiose (4-O-β-D-glucopyranosyl-β-D-glucopyranose), which can interconvert in solution and reach equilibrium. In this application, the term "cellobiose" has its broad meaning and covers other possible chemical forms known in the art, including but not limited to its isomers, such as α-type and β-type, hydrated forms, etc. In this application, serum cellobiose detection includes detecting total cellobiose (the sum of α-type and β-type) or detecting one of the isomers alone.

[0026] As used in this article, the term “Metabolically Healthy Obesity” (MHO) refers to an obese individual (BMI ≥ 28.0 kg / m²) who meets fewer than two of the following metabolic phenotype grouping criteria and has no history of related drug (antihypertensive, hypoglycemic, lipid-lowering) treatment: (1) Hypertension: systolic blood pressure ≥ 140 mmHg and / or diastolic blood pressure ≥ 90 mmHg, or a history of hypertension and treatment with antihypertensive drugs; (2) Glucose metabolism disorder: fasting blood glucose > 7.0 mmol / L, or HbA1c > 6.5%, or 2-hour blood glucose in an oral glucose tolerance test > 11.1 mmol / L, or a history of diabetes or treatment with hypoglycemic drugs; (3) Lipid metabolism disorder: serum TG > 1.7 mmol / L, serum HDL-C < 1.0 mmol / L, or a history of targeted lipid-lowering treatment. Individuals with moderate obesity (MHO) are characterized by relatively normal insulin sensitivity despite obesity (BMI ≥ 28.0 kg / m²), without significant metabolic syndrome symptoms, and have a relatively low short-term risk of cardiovascular disease and type 2 diabetes. However, MHO is not a stable condition; studies show that approximately 30-50% of MHO individuals will transition to melanocytic ulcer (MUO) within 3-5 years. Therefore, regular monitoring and early intervention for MHO individuals are of significant clinical importance.

[0027] As used in this article, the term "metabolic unhealthy obesity" (MUO) refers to obese individuals (BMI ≥ 28.0 kg / m²) who meet two or more of the above-mentioned metabolic phenotype grouping criteria, or who have a history of a confirmed diagnosis of metabolic syndrome and related drug treatment. MUO individuals are characterized by significant metabolic disorders on the basis of obesity, including pathophysiological changes such as insulin resistance, chronic low-grade inflammation, and oxidative stress. They have a significantly increased risk of developing complications such as metabolic syndrome, type 2 diabetes, non-alcoholic fatty liver disease, and atherosclerotic cardiovascular disease. MUO individuals require active metabolic interventions, including lifestyle modifications and drug treatment.

[0028] As used herein, the terms “level,” “concentration,” and “content” are used interchangeably, and when used in conjunction with a specific marker (e.g., “cellobiose”), both indicate the amount of a component (e.g., “cellobiose”) present in a composition, mixture, or system, whether expressed as a percentage by mass, a percentage by mole, a percentage by volume, parts by weight, molar concentration, or any other suitable unit of measurement.

[0029] As used herein, the term "serum biomarker" refers to various molecules and their metabolites present in serum, including but not limited to: fatty acids (such as palmitic acid, oleic acid, linoleic acid, arachidonic acid, etc.), triglycerides, phospholipids (such as phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, etc.), sphingolipids (such as ceramides, sphingomyelin, etc.), cholesterol and its esters, eicosanoic acids (such as prostaglandins, leukotrienes, etc.), cellobiose, branched-chain amino acids (leucine, isoleucine, valine), glycolic acid, 4-hydroxypropionic acid, bile acid metabolites (primary bile acids, secondary bile acids), palmitic acid, stearic acid, free fatty acids, malondialdehyde (MDA), carnitine, tryptophan metabolites, glutamine, glycine, serine, etc.; these molecules and their metabolites play important roles in energy metabolism, cell membrane structure, and signal transduction. In some embodiments, serum biomarkers are serum cellobiose or composed of it. In some embodiments, the serum biomarker further comprises other serum biomarkers known or to be known in the art, including but not limited to branched-chain amino acids (leucine, isoleucine, valine), glycolic acid, 4-hydroxypropionic acid, bile acid metabolites (primary bile acids, secondary bile acids), sphingolipids, phospholipids, palmitic acid, stearic acid, free fatty acids, malondialdehyde (MDA), carnitine, tryptophan metabolites, glutamine, glycine, and serine. In some embodiments, the serum biomarker comprises serum cellobiose and other metabolite biomarkers selected from at least one of branched-chain amino acids (leucine, isoleucine, valine), glycolic acid, 4-hydroxypropionic acid, bile acid metabolites (primary bile acids, secondary bile acids), sphingolipids, phospholipids, palmitic acid, stearic acid, free fatty acids, malondialdehyde (MDA), carnitine, tryptophan metabolites, glutamine, glycine, and serine.

[0030] As used herein, the term "cutoff value" refers to a biomarker concentration threshold used to distinguish between two different clinical states (e.g., MHO and MUO). In one embodiment of this application, the cutoff value is a value with the optimal Youden Index determined by receiver operating characteristic (ROC) curve analysis. The Youden Index is defined as the sum of sensitivity and specificity minus 1, i.e., J = sensitivity + specificity - 1. The Youden Index ranges from 0 to 1, with a higher index indicating better diagnostic efficacy. In one embodiment of this application, the cutoff value for serum cellobiose is 50-60 μmol / L; in a preferred embodiment, the cutoff value for serum cellobiose is 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, or any value between any two numbers; in one embodiment, the cutoff value for serum cellobiose is 57.71 μmol / L. In one embodiment of this application, when the subject's serum cellobiose is higher than the cutoff value, it is determined to be MUO; when it is lower than or equal to the cutoff value, it is determined to be MHO.

[0031] One aspect of this application relates to the use of serum biomarker detection reagents in the preparation of products for obesity typing, obesity prognosis assessment, obesity management or precision treatment of obesity, wherein the serum biomarker comprises serum cellobiose.

[0032] In some embodiments, the obesity classification includes classifying obese individuals into metabolically healthy obesity (MHO) or metabolically unhealthy obesity (MUO) based on serum biomarker levels. For example, when the serum biomarker level is above a cutoff value, the individual is classified as metabolically unhealthy obesity (MUO), and when it is below or equal to the cutoff value, the individual is classified as metabolically healthy obesity (MHO). In some embodiments, the obesity prognostic assessment includes assessing obesity prognosis based on the obesity classification results; for example, for individuals with metabolically healthy obesity (MHO), assessing their risk of transitioning to metabolically unhealthy obesity (MUO); and / or for individuals with metabolically unhealthy obesity (MUO), assessing their risk of developing metabolic syndrome, impaired glucose tolerance, type 2 diabetes, progression of non-alcoholic fatty liver disease (NAFLD), or atherosclerotic cardiovascular disease (ASCVD). In some embodiments, the obesity management includes obesity management based on the obesity classification results; for example, for individuals with metabolically healthy obesity (MHO), a metabolic maintenance monitoring program is developed, including regular monitoring of serum biomarker levels to detect early trends toward metabolically unhealthy obesity (MUO); and / or for individuals with metabolically unhealthy obesity (MUO), an intensive metabolic intervention program is developed, including dynamic evaluation of the intervention effect based on serum biomarker levels. In some embodiments, the precision treatment of obesity includes selecting a treatment strategy based on the obesity classification results; for example, for individuals with metabolically healthy obesity (MHO), a non-pharmacological treatment strategy primarily based on lifestyle interventions is adopted; and / or for individuals with metabolically unhealthy obesity (MUO), a combination of drug therapy and lifestyle interventions is adopted, for example, the drug therapy includes insulin sensitizers, GLP-1 receptor agonists, SGLT2 inhibitors, or metabolic surgery.

[0033] In some embodiments, the cutoff value used to classify obese individuals as metabolically healthy obesity (MHO) or metabolically unhealthy obesity (MUO) is the value with the maximum Yangen index determined by receiver operating characteristic (ROC) analysis. In one embodiment, the cutoff value is 50-60 μmol / L. In another embodiment, the cutoff value is 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, or any value between any two numbers. In a preferred embodiment, the cutoff value is 57.71 μmol / L. In yet another embodiment, the cutoff value used to classify obese individuals as metabolically healthy obesity (MHO) or metabolically unhealthy obesity (MUO) is the cellobiose level with the maximum Yangen index determined by receiver operating characteristic (ROC) analysis. In one embodiment, the cutoff value for cellobiose level is 50-60 μmol / L. In one embodiment, the cutoff value for cellobiose level is 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, or any value between any two numbers. In a preferred embodiment, the cutoff value for cellobiose level is 57.71 μmol / L.

[0034] In some embodiments, the serum biomarker levels are obtained from the subject's fresh serum, refrigerated serum, or frozen serum. In some embodiments, the fresh serum is serum collected within 4 hours without cryopreservation before testing; in some embodiments, the frozen serum is serum collected, frozen at -80°C, and then thawed before testing. In some embodiments, the serum biomarker levels are obtained from the subject's peritoneal serum or non-fasting serum; for example, the fasting serum is obtained by separating venous blood collected after the subject has fasted for more than 12 hours.

[0035] In the above embodiments, the serum biomarker levels include, but are not limited to, serum cellobiose levels. In some embodiments, the serum cellobiose levels are obtained from the subject's fresh serum, refrigerated serum, or frozen serum. In some embodiments, the fresh serum is serum collected within 4 hours without cryopreservation before testing; in some embodiments, the frozen serum is serum collected, frozen at -80°C, and then thawed before testing. In some embodiments, the serum biomarker levels are obtained from the subject's peritoneal serum or non-fasting serum; for example, the fasting serum is obtained by separating venous blood collected after the subject has fasted for more than 12 hours. In some embodiments, the serum cellobiose is α-cellobiose, β-cellobiose, or a combination thereof.

[0036] In some embodiments, the serum biomarker levels also include serum cellobiose and levels of at least one or more selected from branched-chain amino acids (leucine, isoleucine, valine), glycolic acid, 4-hydroxypropionic acid, bile acid metabolites (primary bile acids, secondary bile acids), sphingolipids, phospholipids, palmitic acid, stearic acid, free fatty acids, malondialdehyde (MDA), carnitine, tryptophan metabolites, glutamine, glycine, and serine.

[0037] In some implementations, the object can be a mammal, such as a human, a non-human primate (e.g., ape, orangutan), a rodent (e.g., rat, mouse, guinea pig), a pet (e.g., cat, dog), or a livestock (e.g., horse, cow, sheep, pig, rabbit).

[0038] In some embodiments, the serum biomarker detection reagent includes reagents for liquid chromatography-mass spectrometry (LC-MS / MS), gas chromatography-mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC), electrochemical detection, and other relevant detection reagents known or to be known in the art, provided that such other detection reagents can achieve quantitative, semi-quantitative, and / or qualitative typing of the level, content, and / or concentration of a specific serum biomarker. In some embodiments, the serum biomarker detection reagent includes reagents for detecting serum cellobiose levels, for example, reagents for liquid chromatography-mass spectrometry (LC-MS / MS), gas chromatography-mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC), or electrochemical detection of serum cellobiose levels.

[0039] One aspect of this application relates to a kit for obesity typing, obesity prognostic assessment, obesity management, or precision treatment of obesity, comprising assay reagents for detecting serum biomarkers, including serum cellobiose; and instructions for use describing a method for distinguishing metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) based on cutoff values, and clinical management recommendations based on this typing. In some embodiments, the kit optionally includes standards, such as standard solutions of serum biomarkers at serial concentrations and / or other assay reagents for obesity typing. In some embodiments, the cutoff value described in the instructions is 50-60 μmol / L, and / or the cutoff value is 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, or any value between any two numbers, and / or the cutoff value is 57.71 μmol / L. One aspect of this application relates to a kit for obesity classification, obesity prognostic assessment, obesity management, or precision treatment of obesity, comprising: (i) a assay reagent for detecting serum cellobiose levels; (ii) a standard, such as a series of cellobiose standard solutions; and (iii) instructions for use, which describe a method for distinguishing metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) by a cutoff value, and clinical management recommendations based on this classification. In one embodiment, the instructions for use also describe a metabolic deterioration risk assessment table for predicting the probability of conversion from MHO to MUO based on the rate of change in serum cellobiose levels.

[0040] One aspect of this application relates to a system for obesity typing, obesity prognostic assessment, obesity management, or precision obesity treatment. In some embodiments, the system for obesity typing, obesity prognostic assessment, obesity management, or precision obesity treatment includes: (i) A data receiving module, used to receive or input data on the level of biomarkers in the serum of a subject, wherein the biomarkers include serum cellobiose; (ii) Judgment module, which integrates an obesity classification model, wherein the classification model includes a cutoff value and compares the serum cellobiose level with the cutoff value; (iii) Output module, used to output obesity metabolic classification results based on the comparison results of the judgment module; wherein, when the serum cellobiose level is greater than the cutoff value, the classification result of metabolically unhealthy obesity (MUO) is output, and optionally, suggestions for strengthening metabolic intervention are also output; when it is less than or equal to the cutoff value, the classification result of metabolically healthy obesity (MHO) is output, and optionally, suggestions for metabolic maintenance monitoring are also output.

[0041] In some embodiments, the system further includes a detection module for detecting the level of serum cellobiose, for example, the detection module includes a liquid chromatography-tandem mass spectrometer equipped with an electrospray ionization (ESI) source. In some embodiments, the system further includes a data processing module for peak identification, internal standard calibration, and concentration calculation of the raw mass spectrometry data obtained by the detection module, generating serum cellobiose level data and transmitting it to the data receiving module. In some embodiments, the system also includes an output module for outputting obesity prognostic assessment information, obesity management recommendations, or precision obesity treatment strategies based on the comparison results of the judgment module. In some embodiments, the obesity prognostic assessment information includes, for metabolically healthy obese (MHO) individuals, an output of a risk assessment of conversion to metabolically unhealthy obese (MUO); and for metabolically unhealthy obese (MUO) individuals, an output of a risk assessment of the development of metabolic syndrome, impaired glucose tolerance, type 2 diabetes, progression of non-alcoholic fatty liver disease, or atherosclerotic cardiovascular disease. In some implementations, the clinical obesity management recommendations include: for individuals with metabolically healthy obesity (MHO), a metabolic maintenance monitoring program is provided, including regular monitoring of serum cellobiose levels to detect early trends toward metabolically unhealthy obesity (MUO); for individuals with metabolically unhealthy obesity (MUO), an intensive metabolic intervention program is provided, including dynamic assessment of the intervention's effectiveness based on serum cellobiose levels. In some implementations, the precision obesity treatment strategy includes, for individuals with metabolically healthy obesity (MHO), a non-pharmacological treatment strategy primarily based on lifestyle interventions; and for individuals with metabolically unhealthy obesity (MUO), a strategy combining pharmacological treatment with lifestyle interventions, wherein the pharmacological treatment includes insulin sensitizers, GLP-1 receptor agonists, SGLT2 inhibitors, or metabolic surgery.

[0042] One aspect of this application relates to a method for classifying obesity by in vitro detection of serum cellobiose. In some embodiments, the method includes collecting a fasting serum sample from an obese subject; detecting the cellobiose content in the serum sample using liquid chromatography-tandem mass spectrometry; comparing the detected cellobiose content with a cutoff value; and classifying the subject as metabolically healthy obesity or metabolically unhealthy obesity based on the comparison result.

[0043] One aspect of this application relates to a method for assessing the prognosis of obesity by detecting serum cellobiose in vitro. In some embodiments, the method includes collecting fasting serum samples from obese subjects; detecting the cellobiose content in the serum samples using liquid chromatography-tandem mass spectrometry; comparing the detected cellobiose content with a cutoff value; classifying the subjects into metabolically healthy obesity or metabolically unhealthy obesity based on the comparison results; for metabolically healthy obese individuals, assessing their risk of transitioning to metabolically unhealthy obesity, the risk assessment including calculating the rate of change in serum cellobiose levels, with an annual increase greater than 15 μmol / L or a level exceeding 50 μmol / L considered high risk; for metabolically unhealthy obese individuals, assessing their risk of developing metabolic syndrome, impaired glucose tolerance, type 2 diabetes, progression of non-alcoholic fatty liver disease, or atherosclerotic cardiovascular disease, the risk assessment including calculating a risk score based on serum cellobiose levels combined with age, sex, BMI, blood glucose, and blood lipid indicators.

[0044] One aspect of this application relates to a method for obesity management using in vitro detection of serum cellobiose. In some embodiments, the method includes collecting fasting serum samples from obese subjects; detecting the cellobiose content in the serum samples using liquid chromatography-tandem mass spectrometry; comparing the detected cellobiose content with a cutoff value; classifying the subjects into metabolically healthy obesity or metabolically unhealthy obesity based on the comparison results; developing a differentiated management plan based on the subjects' obesity classification; for metabolically healthy obese individuals, developing a metabolic maintenance monitoring plan, including detecting serum cellobiose levels every 6 months, conducting a comprehensive metabolic indicator check annually, and initiating an alert when the cellobiose level increases by more than 10% or exceeds 50 μmol / L twice consecutively; for metabolically unhealthy obese individuals, developing an intensive metabolic intervention plan, including detecting serum cellobiose levels every 3 months to dynamically assess the intervention effect, and adjusting the treatment intensity according to the decrease in cellobiose levels.

[0045] One aspect of this application relates to a precision treatment method for obesity based on in vitro detection of serum cellobiose levels. In some embodiments, the method includes collecting fasting serum samples from obese subjects; detecting the cellobiose content in the serum samples using liquid chromatography-tandem mass spectrometry (LC-MS / MS); comparing the detected cellobiose content with a cutoff value; classifying the subjects as metabolically healthy or metabolically unhealthy obese based on the comparison results; for metabolically healthy obese individuals, implementing a non-pharmacological treatment strategy primarily based on lifestyle interventions, including adopting a Mediterranean or DASH diet pattern, at least 150 minutes of moderate-intensity aerobic exercise per week, and establishing healthy sleep habits through cognitive behavioral therapy; and considering initiating drug therapy when the BMI is greater than 30 kg / m² and accompanied by other risk factors. In some embodiments, the method includes collecting fasting serum samples from obese subjects; detecting the cellobiose content in the serum samples using LC-MS / MS; comparing the detected cellobiose content with a cutoff value; classifying the subjects as metabolically healthy or metabolically unhealthy obese based on the comparison results; and for metabolically unhealthy obese individuals, implementing a comprehensive treatment strategy combining drug therapy and lifestyle interventions. In some implementations, medications are selected based on the primary metabolic disorder type. For example, insulin sensitizers are used for those with predominantly insulin resistance, GLP-1 receptor agonists or SGLT2 inhibitors are used for those with elevated blood glucose, and statins or fibrates are used for those with dyslipidemia. In some implementations, metabolic surgery is evaluated for indications in individuals with a BMI greater than 35 kg / m² or a BMI greater than 30 kg / m² with type 2 diabetes who have not responded well to lifestyle interventions and medication.

[0046] One aspect of this application relates to a method for constructing an obesity classification model. In some embodiments, the method includes collecting fasting serum samples from metabolically healthy obese individuals and metabolically unhealthy obese individuals; detecting the content of cellobiose in the serum samples using liquid chromatography-tandem mass spectrometry; and determining a cutoff value to distinguish between metabolically healthy and metabolically unhealthy obesity through receiver operating characteristic (ROC) curve analysis.

[0047] Those skilled in the art can combine the technical solutions and features described herein in any way without departing from the inventive concept and protection scope of this invention. Other aspects of this invention will be apparent to those skilled in the art from the disclosure herein.

[0048] Example

[0049] The present invention will be described in detail below with reference to specific embodiments, but the scope of protection of the present invention is not limited to these embodiments. Those skilled in the art can make various modifications and variations to the present invention without departing from the principles of the invention, and all such modifications and variations should be included within the scope of protection of the present invention.

[0050] Unless otherwise specified, experimental methods in the following examples are generally performed under the conditions described in the standard description, or under standard conditions, or under the conditions recommended by the manufacturer. Percentages and parts are by weight unless otherwise stated.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as are familiar to one skilled in the art. Furthermore, any methods and materials similar to or equivalent to those described herein may be used in this application. The preferred embodiments and materials described herein are for illustrative purposes only.

[0052] Example 1: Sample Collection and Processing

[0053] This embodiment details the inclusion / exclusion criteria, cohort division, sample collection, processing and storage methods, and the extraction and preparation methods of serum metabolites.

[0054] All participants in this study were recruited from the Department of Endocrinology at Zhongshan Hospital affiliated with Fudan University. All participants signed written informed consent forms before enrollment, and the study protocol was reviewed and approved by the ethics committee.

[0055] I. Inclusion and Exclusion Criteria

[0056] 1. Inclusion criteria

[0057] Subjects must meet both the following baseline criteria and metabolic phenotype grouping criteria: 1. Basic criteria: (1) Age 18 to 70 years old; (2) Body Mass Index (BMI) ≥ 28.0 kg / m² according to the Chinese adult obesity diagnostic criteria.

[0058] 2. Metabolic Phenotypic Grouping Criteria: Subjects were divided into two groups based on four metabolic components: blood pressure, fasting blood glucose, triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C). The definitions of abnormal metabolic indicators were as follows: (1) Hypertension: systolic blood pressure ≥140 mmHg and / or diastolic blood pressure ≥90 mmHg, or a history of hypertension and receiving antihypertensive drug treatment; (2) Glucose metabolism disorder: meeting at least one of the following criteria: FBG > 7.0 mmol / L, HbA1c > 6.5%, or 2-hour post-oral glucose tolerance test (OGTT-2h) > 11.1 mmol / L; (3) Previous diagnosis of diabetes or treatment with hypoglycemic drugs; (4) Lipid metabolism disorder: serum TG>1.7 mmol / L, serum HDL-C<1.0 mmol / L or previous targeted lipid-lowering treatment.

[0059] 1.3. Grouping Definition: (1) Metabolic healthy obesity (MHO) group: meet <2 of the above metabolic phenotype grouping criteria and have no history of related drug (antihypertensive drugs, hypoglycemic drugs, lipid-regulating drugs) treatment; (2) Metabolic unhealthy obesity (MUO) group: meet ≥2 of the above metabolic phenotype grouping criteria, or have a history of diagnosis of metabolic syndrome and related drug treatment.

[0060] 2. Exclusion Criteria

[0061] Applicants meeting any of the following criteria will be excluded: (1) Has a history of or currently has any histological type of malignancy; (2) Previously diagnosed with serious cardiovascular diseases (CVD) such as coronary heart disease, myocardial infarction, stroke, and heart failure; (3) Patients with a history of type 1 or type 2 diabetes (exclusion only for the MHO group; patients with a history of diabetes should not be included in the MHO group). (4) Female subjects who are pregnant or lactating; (5) Severe liver dysfunction (AST / ALT > 3 times the upper limit of normal) or renal insufficiency (eGFR < 60 mL / min / 1.73 mcg) 2 ); (6) Use of antibiotics or probiotic preparations within the past 3 months, or participation in other clinical trials, or other circumstances deemed unsuitable for participation in this study by the researchers.

[0062] II. Queue Division

[0063] This study included 96 participants, with 47 in the MHO group and 49 in the MUO group. To construct and validate the model's effectiveness, stratified random sampling was used to divide the participants into training and validation sets in a 7:3 ratio. The training set consisted of 66 participants (33 in the MHO group and 33 in the MUO group), and the validation set consisted of 30 participants (14 in the MHO group and 16 in the MUO group), ensuring that baseline characteristics such as gender, age, and BMI were balanced and comparable between the two groups.

[0064] III. Blood Sample Collection and Pretreatment

[0065] All subjects were required to fast for at least 12 hours prior to blood collection. The blood collection window was strictly controlled between 8:00 AM and 10:00 AM. Approximately 5 mL of whole blood was collected via antecubital vein puncture using a standard medical procoagulant vacuum blood collection tube. The collected whole blood sample was centrifuged at 3000 rpm for 10 minutes for serum separation. After centrifugation, the supernatant pale yellow clear serum was carefully aspirated into cryovials and immediately transferred to a -80°C freezer for storage.

[0066] IV. Sample Quality Control

[0067] Serum samples were collected from 96 obese subjects included in this study (47 in the MHO group and 49 in the MUO group) using the method described above. Sample quality assessment results showed that: (1) QC sample quality control: The QC samples inserted into the sample queue were tested and the relative standard deviation (RSD) of the main metabolites was <15%, indicating that the instrument system was stable and the experimental data was reliable. (2) Sample integrity: All 96 samples were successfully pretreated without sample loss and the sample recovery rate was >95%; (3) Metabolite extraction efficiency: By adding internal standard, the metabolite extraction recovery rate was between 85% and 110%, which meets the requirements of metabolomics analysis.

[0068] Example 2: Non-targeted metabolomics detection

[0069] This embodiment aims to use LC-MS / MS to perform non-targeted metabolomics detection, comprehensively detect the metabolite profiles of serum from MHO and MUO patients in the training set, screen metabolites with significant differences between the two groups, and provide candidate biomarkers for subsequent targeted validation and model construction.

[0070] The serum sample obtained in Example 1 was slowly thawed at 4°C. 100 μL of serum was added to 400 μL of pre-cooled methanol / acetonitrile / water solution (volume ratio 2:2:1), vortexed for 3 minutes, sonicated at low temperature for 30 minutes, allowed to stand at -20°C for 10 minutes, and centrifuged at 14000g and 4°C for 20 minutes. The supernatant was vacuum dried, and before mass spectrometry analysis, 100 μL of acetonitrile aqueous solution (acetonitrile:water = 1:1, v / v) was added to reconstitute the solution. The mixture was vortexed, centrifuged at 14000g and 4°C for 15 minutes, and the supernatant was collected for analysis.

[0071] I. Chromatographic Separation

[0072] Samples were separated using a Vanquish LC ultra-high performance liquid chromatography (UHPLC) system with a HILIC column. The gradient elution program was as follows: 0–0.5 min, 95% B; 0.5–7 min, B linearly decreasing from 95% to 65%; 7–8 min, B linearly decreasing from 65% to 40%; 8–9 min, B maintained at 40%; 9–9.1 min, B linearly decreasing from 40% to 95%; 9.1–12 min, B maintained at 95%. Throughout the analysis, the sample was placed in an autosampler at 4°C. Specific chromatographic conditions are shown in Table 1.

[0073] Table 1. Chromatographic separation conditions

[0074] II. Mass Spectrometry Detection

[0075] After separation using a Vanquish LC ultra-high performance liquid chromatography (UHPLC) system, the samples were analyzed by mass spectrometry using an OrbitrapExploris™ 480 mass spectrometer (Thermo Fisher Scientific), employing both positive and negative electrospray ionization (ESI) modes. Specific mass spectrometry parameters / conditions are shown in Table 2.

[0076] Table 2. Mass spectrometry detection parameters / conditions

[0077] III. Quality Control

[0078] To avoid the influence of fluctuations in instrument detection signals, samples are analyzed continuously in a random order. QC samples are inserted into the sample queue to monitor and evaluate the stability of the system and the reliability of the experimental data.

[0079] IV. Data Processing and Statistical Analysis

[0080] The raw data was converted to .mzXML format using ProteoWizard, and then peak alignment, retention time correction, and peak area extraction were performed using XCMS software. The data extracted by XCMS were first subjected to metabolite structure identification and data preprocessing (null filtering: removing ion peaks with missing values ​​>50%; null filling: KNN filling; data filtering: filtering features with RSD >50%), followed by experimental data quality evaluation.

[0081] V. Difference Analysis

[0082] Approximately 17,169 metabolic characteristic peaks were detected from the training set samples using non-targeted metabolomics detection. After data preprocessing, about 1,108 high-quality metabolic features were retained for subsequent analysis. Correlation analysis of QC samples showed that the metabolic profiles of all QC samples were highly consistent (Pearson correlation coefficient r > 0.90), indicating good instrument system stability and experimental repeatability.

[0083] The metabolic profiles of the MHO and MUO groups in the training set were compared using orthogonal partial least squares discriminant analysis (OPLS-DA). The results are as follows: Figure 1 As shown, the MHO and MUO groups exhibited a clear separation trend on the OPLS-DA score map. The MHO group samples were mainly distributed in the negative region (left side), while the MUO group samples clustered in the positive region (right side). The 95% confidence intervals of the two groups showed almost no overlap, and the difference between the groups was significant (R²Y=0.96, Q²=0.62). The permutation test (n=1000) confirmed that the model did not overfit (p<0.01). These results indicate a statistically significant difference in the serum metabolic profiles of the two groups, suggesting the existence of potential metabolic biomarkers that can be used to differentiate between MHO and MUO.

[0084] Then, GraphPad Prism 10 was used to perform independent samples Stein t-test on the serum metabolites of the two groups and calculate the fold change (FC). The criteria for statistical significance were p < 0.05, |log2(FC)|>2, and VIP>1. Metabolites with significant expression differences between the two groups were screened out.

[0085] As shown in Table 3 below, a total of 34 differentially expressed metabolites were identified, of which 29 were upregulated and 5 were downregulated in the MUO group.

[0086] Table 3 Differential metabolites in the MUO group

[0087] Because non-target metabolism has the potential for false positives, metabolites were first screened based on a score ≥0.95. Metabolites with scores below this level were excluded due to the possibility of false positives. After screening, 16 metabolites remained. These metabolites were then systematically and quantitatively evaluated from four dimensions: statistical performance, biological specificity, clinical resistance to interference, and detection stability. Ultimately, cellobiose was determined as the sole analyte for high-throughput targeted metabolism detection.

[0088] Cellobiose not only possesses excellent statistical characteristics, including extremely significant (-Log10(p)=4.44, p<0.000036), high group contribution (VIP=2.60), moderate variation range, and clear biological significance (log2(FC)=1.00), but also exhibits strong clinical anti-interference ability, good detection stability, and the outstanding advantage of serving as both a diagnostic biomarker and a therapeutic target. The remaining 15 candidate biomarkers were either exogenous pollutants / drug residues unrelated to obesity metabolic status, or highly influenced by non-disease factors such as diet, gender, age, and fasting, or suffered from poor specificity and unclear mechanisms, failing to meet the stringent requirements of high-throughput targeted metabolic detection. Therefore, cellobiose was ultimately selected as the analyte for subsequent high-throughput targeted metabolic detection.

[0089] Example 3: High-throughput targeted metabolic detection and biomarker validation

[0090] This embodiment aims to use high-throughput targeted metabolism detection technology to accurately quantify and validate the candidate metabolites screened in Example 2, determine the differences in their content in the serum of MHO and MUO patients in the training and validation sets, and provide accurate quantitative data for model construction and subsequent validation.

[0091] The same sample pretreatment method as in Example 2 was used to ensure the comparability of non-target and targeted detection results.

[0092] I. Chromatographic Separation

[0093] Samples were separated using an Agilent 1290 Infinity LC ultra-high performance liquid chromatography system, employing both HILIC and C18 columns. Specific chromatographic conditions are shown in Tables 4 and 5.

[0094] Table 4. Chromatographic separation conditions (HILIC column)

[0095] The gradient elution program for the HILIC column is as follows: 0–1.0 min, 85% B; 1.0–3.0 min, B linearly changes from 85% to 80%; 3.0–4.0 min, 80% B; 4.0–6.0 min, B linearly changes from 80% to 70%; 6.0–10.0 min, B linearly changes from 70% to 50%; 10–15.5 min, B remains at 50%; 15.5–15.6 min, B linearly changes from 50% to 85%; 15.6–23 min, B remains at 85%.

[0096] Table 5. Chromatographic separation conditions (C18 column)

[0097] The gradient elution program for the C18 column is as follows: 0–5 min, B changes linearly from 5% to 60%; 5–11 min, B changes linearly from 60% to 100%; 11–13 min, B remains at 100%; 13–13.1 min, B changes linearly from 100% to 5%; 13.1–16 min, B remains at 5%.

[0098] II. Mass Spectrometry Detection

[0099] Mass spectrometry analysis was performed using an AB 6500+ QTRAP mass spectrometer (AB SCIEX). Specific mass spectrometry parameters / conditions are shown in Table 6.

[0100] Table 6. Mass Spectrometry Parameters / Conditions

[0101] III. Quality Control

[0102] To avoid the influence of fluctuations in instrument detection signals, samples are analyzed sequentially in a random order. QC samples (prepared by mixing all samples of equal volume) are inserted into the sample queue to monitor and evaluate the stability of the system and the reliability of the experimental data.

[0103] IV. Quantitative Methods

[0104] Multiquant software was used to extract peaks from the raw MRM data, and the ratio of the peak area of ​​each substance to the internal standard peak area was calculated. The content (μmol / L) of each substance was then calculated based on the standard curve. The extracted data were first subjected to quality evaluation, and data analysis was performed only after the evaluation was passed.

[0105] V. Experimental Results

[0106] The cellobiose in the training and validation sets was quantitatively detected using a targeted detection method, and the results are shown in Table 7.

[0107] Table 7 Serum cellobiose content

[0108] Figure 2 The graph shows the difference in serum cellobiose levels between the MHO and MUO groups in the training set, illustrating the comparison between the serum cellobiose levels of the MHO group (n=33) and the MUO group (n=33). The independent samples t-test results showed that the serum cellobiose level in the MUO group was significantly higher than that in the MHO group, and the difference was statistically significant. p<0.0001). This targeted quantification result is highly consistent with the non-targeted screening results, confirming that serum cellobiose does indeed show a significant upward trend in MUO patients. This cross-platform consistency validation eliminates possible false positive results in non-targeted testing, confirming that serum cellobiose is indeed a differential metabolite between MHO and MUO.

[0109] Figure 3 The graph shows the difference in serum cellobiose levels between the MHO and MUO groups in the validation set, and presents the comparison results of serum cellobiose levels between the MHO group (n=14) and the MUO group (n=16). The independent samples t-test results show that the serum cellobiose level in the MUO group was significantly higher than that in the MHO group, and the difference was statistically significant. , p <0.05). The above results show that the serum cellobiose content in the MUO group was significantly higher than that in the MHO group in both the training and validation sets (p<0.05), and the fold difference between the two groups was similar (1.49-fold in the training set and 1.53-fold in the validation set). This consistent result across datasets strongly demonstrates the stability and reproducibility of serum cellobiose content as a biomarker.

[0110] Example 4: Construction and Validation of Fractal Model

[0111] This embodiment aims to utilize the quantitative data obtained in Example 3 to construct a model based on serum cellobiose content, determine the optimal cutoff value, validate the diagnostic efficacy of the model in the validation set, and evaluate its application value as a clinical auxiliary tool.

[0112] I. Training Set Model Construction

[0113] Receiver operating characteristic (ROC) curves were constructed using the binary clinical outcomes of subjects in the training set (33 cases of MHO and 33 cases of MUO) and the serum cellobiose content data measured in Example 3. The area under the curve (AUC) was calculated to evaluate the model's discriminative power. The ROC curves constructed based on the training set data are shown below. Figure 4 As shown, the area under the ROC curve (AUC) is 0.81 (95% confidence interval: 0.72–0.91), indicating that the model has good discriminative ability.

[0114] Based on the principle of maximizing the Youden index, the optimal diagnostic cut-off value was determined to be 57.71 μmol / L. The results showed that the Youden index was maximized when the cut-off value was set to 57.71 μmol / L. The diagnostic efficacy (sensitivity and specificity) of the genotyping model on the training set was calculated.

[0115] In the 2×2 contingency table of the training set, MUO was used as the target positive group and MHO as the negative control group, with true positives (TP) = 26, false negatives (FN) = 11, true negatives (TN) = 28, and false positives (FP) = 5. Sensitivity was calculated using the formula "number of true positives / total number of target positives (TP / (TP + FN))", yielding 26 / 37 ≈ 70.27%; specificity was calculated using the formula "number of true negatives / total number of negative control groups (TN / (TN + FP))", yielding 28 / 33 ≈ 84.85%. At this cutoff value, the model's sensitivity was 0.70 and specificity was 0.85, achieving a good balance between sensitivity and specificity.

[0116] Although 57.71 μmol / L was determined to be the optimal cutoff value for distinguishing between MHO and MUO, considering the different requirements for detection performance in different clinical application scenarios, the diagnostic efficacy of serum cellobiose was further verified over a wider concentration range to determine a clinically applicable cutoff value selection range.

[0117] Table 8. Diagnostic efficacy corresponding to different cutoff values

[0118] When the cutoff value is reduced to 55 μmol / L, the estimated sensitivity increases to about 75%, while the estimated specificity decreases to about 67%, making it suitable for population screening scenarios where maximum detection of MUO is required. If the cutoff value is further reduced to 54 μmol / L, the estimated specificity will drop to <65%, and the false positive rate will be too high, leading to a decrease in clinical applicability.

[0119] When the cutoff value is increased to 59, the estimated specificity increases to about 86%, while the estimated sensitivity decreases to about 62%, which is suitable for preoperative diagnosis scenarios where misdiagnosis needs to be avoided. If it is further increased to 60 μmol / L, according to the curve trend, the sensitivity will decrease to <60%, and the false negative rate will exceed 40%, which does not meet the basic clinical requirements.

[0120] II. Independent Validation of Validation Set

[0121] Receiver operating characteristic (ROC) curves were constructed using the binary clinical outcomes of subjects in the validation set (14 cases of MHO and 16 cases of MUO) and the serum cellobiose content data measured in Example 3, and the area under the curve (AUC) was calculated.

[0122] To verify the stability and reliability of the above diagnostic model, the determined cutoff value (57.71 μmol / L) was independently validated in the validation set cohort. The cutoff value determined in the training set was applied to the validation set samples (14 MHO cases and 16 MUO cases), and the corresponding diagnostic efficacy indicators were calculated. The serum cellobiose content and classification results in the validation set are shown in Table 8 below.

[0123] Table 8. Validation results of serum cellobiose content and classification.

[0124] In the 2×2 contingency table of the validation set, with a cutoff value of 57.71 μmol / L, MUO was used as the target positive group and MHO as the negative control group, where true positives (TP) = 13, false negatives (FN) = 3, true negatives (TN) = 10, and false positives (FP) = 4. Sensitivity was calculated using the formula "number of true positives / total number of target positives (TP / (TP + FN))", yielding a result of 13 / 16 ≈ 81.25%; specificity was calculated using the formula "number of true negatives / total number of negative control groups (TN / (TN + FP))", yielding a result of 11 / 14 ≈ 71.43%. Therefore, the validation set had a sensitivity of 81.3%, a specificity of 71.4%, and a diagnostic accuracy of 76.7%. The results of the validation set were highly consistent with those of the training set, demonstrating the stability and reliability of the cutoff value.

[0125] like Figure 5 As shown, the area under the ROC curve (AUC) of the validation set was 0.85 (95% confidence interval: 0.72–0.99), which was not statistically significantly different from the AUC of the training set (0.81) (using unpaired Delong test). p =0.65, Z=0.45), indicating that the model did not overfit, has good generalization ability, and will not cause a significant decrease in diagnostic efficacy due to sample changes, thus meeting the practical application requirements of clinical auxiliary diagnosis.

[0126] In summary, this embodiment successfully constructed an obesity classification model based on serum cellobiose concentration. The AUC values ​​demonstrate the model's discriminative ability; the training set AUC was 0.81, and the validation set AUC was 0.79, both falling within the 0.75-0.90 range. According to diagnostic test evaluation criteria, an AUC > 0.75 indicates good diagnostic accuracy. The high consistency between the training and validation set AUCs (difference < 0.02) further demonstrates the model's good generalization ability. Meanwhile, the DeLong test showed no significant difference in AUCs between the training and validation sets (…). pThe Youden index (AUC) of 0.65 demonstrates the model's good generalization ability and its diagnostic efficacy does not significantly decrease due to sample variations, meeting the practical application requirements of clinical auxiliary diagnosis. Furthermore, the cutoff value of 57.71 μmol / L, determined by the Youden index maximization principle, balances sensitivity and specificity. In the validation set, the specificity reaches 71.42%, effectively reducing false positives; the sensitivity in the validation set reaches 81.25%, effectively detecting MUO patients. The stable performance of this cutoff value in the validation set proves its clinical application value. This model achieves a diagnostic efficacy of AUC 0.81-0.85 using only serum cellobiose, which is superior to traditional metabolic indicators, demonstrating that serum cellobiose has high clinical application value as a specific biomarker distinguishing MHO from MUO.

[0127] III. Specific Implementation of the Classification Method

[0128] Based on the above research results, this embodiment provides a specific method for obesity classification, including the following steps: (1) Collect fasting serum samples from the subjects (fasting for ≥12 hours); (2) The content of cellobiose in serum was detected using a method such as LC-MS / MS as described in Example 3; (3) Compare the measured cellobiose content with the cutoff value: - If the cellobiose content is greater than the cutoff value, it is classified as metabolically unhealthy obesity (MUO). - If the cellobiose content is ≤ the cutoff value, it is judged as metabolically healthy obesity (MHO).

[0129] Example 5: Cellular experiments to verify the biological function of metabolites

[0130] This embodiment aims to explore the potential pathophysiological mechanism of cellobiose in the development of MUO through in vitro cell experiments, verify whether it has a biological effect of inducing pancreatic β-cell damage, and provide mechanistic supporting evidence for cellobiose as a biomarker to distinguish MHO from MUO.

[0131] I. Experimental Materials

[0132] The cell line used was the mouse insulinoma cell line (Min6 cells), derived from our research group. Cells were cultured in DMEM high-glucose medium containing 10% fetal bovine serum (FBS), 100 U / mL penicillin, and 100 μg / mL streptomycin, and incubated at 37°C in a 5% CO2 incubator. Cellobiose standards (purity >98%) were purchased from MCE, RIPA lysis buffer from Servicebio, BCA protein quantification kit from Beyotime, and reagents such as anti-Lamin B1 antibody, anti-p53 antibody, and anti-GAPDH antibody from Proteintech.

[0133] II. Experimental Methods

[0134] Cell treatment: Min6 cells were treated at a rate of 5 × 10⁶ cells / year. 5 Cells were seeded at different densities in six-well plates and cultured for 24 h. Then, the cells were exposed to different concentrations (0, 40, 60, 80 μM) of cellobiose medium for 24 h. The 0 μM group was set up as a blank control group.

[0135] Western Blot Detection: After cell treatment, the culture medium was discarded, and the cells were washed three times with PBS. Total protein was extracted using RIPA lysis buffer, and protein concentration was determined by the BCA method. Equal amounts of protein were separated by SDS-PAGE electrophoresis and transferred to a PVDF membrane. After blocking with 5% skim milk powder for 1 h, anti-Lamin B1 antibody (1:1000), anti-p53 antibody (1:1000), or anti-GAPDH antibody (1:4000) were added, and the membrane was incubated overnight at 4°C. After washing with TBST, HRP-labeled secondary antibody was added, and the membrane was incubated at room temperature for 1 h. The cells were developed using ECL chemiluminescence immunoassay, and images were acquired using a gel imaging system. Grayscale analysis was performed using ImageJ software, and the relative expression level was calculated using GAPDH as an internal reference.

[0136] III. Results and Discussion

[0137] The results of Western blot analysis (A) and the scatter plot of quantitative analysis (B) are shown in Figure 6. Compared with the control group (0 μM), the expression level of Lamin B1 protein in Min6 cells treated with high-dose (80 μM) cellobiose was significantly reduced, while the expression level of p53 protein was upregulated in a dose-dependent manner (reaching a peak at 80 μM). This expression pattern suggests that the drug may induce cell cycle arrest or apoptosis in pancreatic β cells by activating the p53-mediated DNA damage response pathway, accompanied by the degradation of the nuclear lamina structural protein Lamin B1.

[0138] Lamin B1 is a core cytoskeletal protein that maintains nuclear morphology and nuclear membrane stability, belonging to the laminin family. Downregulation of Lamin B1 expression is a recognized molecular marker of cellular senescence; its deficiency can lead to nuclear membrane structural disorder, chromatin remodeling, and altered gene expression. Our results show that treatment with high concentrations of cellobiose (60-80 μmol / L) significantly downregulated Lamin B1 protein expression in Min6 cells (reducing it by 13.6% and 29.6%, respectively). This result indicates that cellobiose can disrupt the nuclear membrane integrity of pancreatic β-cells and induce pancreatic β-cell senescence.

[0139] p53 is a key regulator of cellular stress responses and is known as the "guardian of the genome." Under stress conditions such as DNA damage, oxidative stress, and nutrient deficiency, p53 protein is activated and accumulates, maintaining genome stability by inducing cell cycle arrest, DNA repair, or apoptosis. Sustained activation of p53 is also an important marker of cellular senescence. Our results show that high concentrations of cellobiose significantly upregulated p53 protein expression in Min6 cells (44.7% increase at 80 μM), suggesting that cellobiose may induce pancreatic β-cell senescence by activating the p53 signaling pathway.

[0140] Notably, the regulatory effect of cellobiose on Lamin B1 and p53 exhibits a clear dose-response relationship: 40 μmol / L (close to serum levels in MHO individuals) has no significant effect on the expression of either protein, while 60 μmol / L and 80 μmol / L (close to or higher than serum levels in MUO individuals) produce significant regulatory effects. This result is consistent with clinical observations of elevated serum cellobiose levels and decreased pancreatic function in MUO individuals, explaining from a mechanistic perspective why serum cellobiose can serve as an effective biomarker for differentiating between MHO and MUO.

[0141] The above experimental results reveal the potential pathogenic mechanism of high levels of cellobiose in MUO patients: by upregulating the p53 signaling pathway and disrupting the nuclear membrane integrity of pancreatic β cells, it induces cells into a pathological senescence state, thereby impairing insulin secretion and glycemic regulation. This finding provides solid mechanistic evidence for establishing cellobiose as a specific biomarker for differentiating MHO from MUO.

[0142] Example 6: Obesity prognostic assessment based on MHO / MUO classification

[0143] This embodiment describes a prognostic assessment method for obesity based on serum cellobiose levels, including risk assessment of MHO to MUO conversion and risk assessment of MUO complications.

[0144] I. Risk Assessment of MHO to MUO Conversion

[0145] For individuals classified as MHO, in addition to monitoring their current metabolic status, it is also necessary to assess their risk of conversion to MUO. Studies have shown that approximately 30-50% of MHO individuals will convert to MUO within 3-5 years, making early identification of high-risk individuals crucial for preventative intervention. This embodiment establishes a risk assessment model for MHO-to-MUO conversion based on the rate of change in serum cellobiose levels. The specific method is as follows: (1) Baseline detection: Serum cellobiose levels (denoted as cellobiose 0) of MHO individuals were detected at the initial assessment. (2) Regular follow-up: It is recommended to recheck serum cellobiose levels (referred to as cellobiose 1, cellobiose 2...) every 6-12 months. (3) Calculate the rate of change: Calculate the rate of change of cellobiose level (Δcellobiose / Δt), unit: μmol / L / year; (4) Risk assessment: Risk stratification based on the rate of change: - Low risk: Cellobiose levels are stable or declining, or the annual increase is <5 μmol / L; - Medium risk: Cellobiose levels increase by 5-15 μmol / L annually; - High risk: Cellobiose levels increase by >15 μmol / L annually, or the cellobiose level is close to the cutoff value (>57 μmol / L).

[0146] II. Risk Assessment of MUO Complications

[0147] For individuals diagnosed with menorrhagia (MUO), it is necessary to assess their risk of developing metabolic syndrome, impaired glucose tolerance, type 2 diabetes, progression of non-alcoholic fatty liver disease (NAFLD), or atherosclerotic cardiovascular disease (ASCVD). A risk scoring system for MUO complications based on serum cellobiose levels was established. This scoring system comprehensively considers traditional risk factors such as serum cellobiose levels, age, sex, BMI, blood pressure, blood glucose, and blood lipids, and uses a multivariate logistic regression model to calculate the predicted probability of various complications in individuals.

[0148] The specific scoring rules are as follows (taking type 2 diabetes risk as an example): - Serum cellobiose level: ≤40 μmol / L (0 points), 40-57.71 μmol / L (1 point), 57.71-80 μmol / L (2 points), >80 μmol / L (3 points); - Fasting blood glucose: <5.6 mmol / L (0 points), 5.6-6.9 mmol / L (1 point), ≥7.0 mmol / L (2 points); - HbA1c: <5.7% (0 points), 5.7-6.4% (1 point), ≥6.5% (2 points); - Age: <45 years old (0 points), 45-55 years old (1 point), >55 years old (2 points); - BMI: 28-30 kg / m² (0 points), 30-35 kg / m² (1 point), >35 kg / m² (2 points).

[0149] A total score of 0-2 indicates low risk, 3-5 indicates medium risk, and ≥6 indicates high risk.

[0150] Example 7: Obesity Management Method Based on MHO / MUO Subtyping

[0151] This embodiment describes an obesity management method based on obesity classification results, including a metabolic maintenance monitoring program for MHO individuals and an enhanced metabolic intervention program for MUO individuals.

[0152] I. Metabolic Maintenance Monitoring Protocol for MHO Individuals

[0153] For individuals classified as MHO, the management goal is to maintain their current metabolically healthy state and prevent conversion to MUO. Specific management protocols include: (1) Lifestyle guidance: It is recommended to maintain healthy eating habits (low sugar, low fat, high fiber diet) and regular exercise habits (at least 150 minutes of moderate-intensity aerobic exercise per week). (2) Regular monitoring plan: - Serum cellobiose levels are tested every 6 months to assess trends in metabolic status; - Have a comprehensive metabolic checkup once a year (blood pressure, fasting blood glucose, HbA1c, blood lipid profile, liver function, kidney function). - Perform an oral glucose tolerance test (OGTT) and liver ultrasound every 2 years; (3) Warning indicators: If the serum cellobiose level continues to rise (the increase is >10% in two consecutive tests) or exceeds 50 μmol / L, it indicates that the metabolic state may deteriorate, and the monitoring frequency should be increased and early intervention should be considered.

[0154] II. Enhanced Metabolic Intervention Program for Individuals with Mutual Occurrence (MUO)

[0155] For individuals classified as MUO, the management goal is to improve metabolic disorders and reduce the risk of complications. Specific management protocols include: (1) Strengthen lifestyle intervention: Under the guidance of professional nutritionists and sports rehabilitation therapists, develop individualized diet and exercise plans with the goal of losing 5-10% of weight within 6 months; (2) Drug therapy: Select appropriate drugs according to the specific type of metabolic abnormality: - Insulin resistance / type 2 diabetes: Metformin (insulin sensitizer), GLP-1 receptor agonists (such as liraglutide, semaglutide), SGLT2 inhibitors (such as dapagliflozin, empagliflozin). - Dyslipidemia: statins, fibrates; - Hypertension: ACEI / ARB drugs (which also have kidney-protective effects); (3) Dynamic evaluation of intervention effects: - Serum cellobiose levels should be tested every 3 months to assess the effectiveness of the intervention; - If the cellobiose level decreases by more than 20%, it indicates that the intervention is effective and the current treatment plan can be continued; - If the cellobiose level does not change significantly or continues to rise, it indicates that the intervention is not effective and the treatment plan needs to be adjusted; (4) Metabolic surgery assessment: For individuals with MUO and BMI > 35 kg / m² and severe metabolic disorders, if drug treatment is ineffective, metabolic surgery (such as gastric bypass surgery or sleeve gastrectomy) may be considered.

[0156] Example 8: Precision Treatment Methods for Obesity Based on MHO / MUO Subtyping

[0157] This embodiment describes a method for selecting precision treatment strategies based on obesity classification results.

[0158] I. Treatment Strategies for Individuals with MHO

[0159] For individuals classified as MHO, whose metabolic status is relatively healthy, the treatment strategy focuses on lifestyle interventions to avoid unnecessary drug treatment. Specific strategies include: (1) Dietary intervention: Adopt the Mediterranean diet or DASH diet pattern, emphasizing the intake of vegetables, fruits, whole grains, nuts, fish and olive oil, and limiting the intake of red meat, processed meat, sugary drinks and refined carbohydrates; (2) Exercise intervention: at least 150 minutes of moderate-intensity aerobic exercise (such as brisk walking, swimming, or cycling) or 75 minutes of vigorous-intensity aerobic exercise per week, combined with resistance training twice a week; (3) Behavioral intervention: Change unhealthy eating behaviors through cognitive behavioral therapy, establish healthy sleep habits (7-8 hours per night), and manage stress; (4) Drug treatment: Weight loss drugs are generally not recommended unless BMI>30 kg / m² and there are other conditions that require drug intervention.

[0160] II. Treatment Strategies for Individuals with MUO

[0161] For individuals classified as MUO, due to significant metabolic disturbances, a comprehensive treatment strategy combining drug therapy and lifestyle interventions is required. Specific strategies include: (1) Basic treatment: All MUO individuals need to receive intensive lifestyle intervention (same as MHO individuals, but with more stringent goals); (2) Drug therapy selection: Drugs are selected individually based on the main metabolic abnormality type and the risk of complications. - For insulin resistance: Metformin is the first choice, which can be combined with GLP-1 receptor agonists or SGLT2 inhibitors; - Primarily for elevated blood glucose: GLP-1 receptor agonists (with both weight loss and cardiovascular protection effects), SGLT2 inhibitors (with both weight loss and kidney protection effects). - Primarily dyslipidemia: statins (lower LDL-C and cardiovascular risk), fibrates (lower TG and raise HDL-C); - Multiple metabolic disorders: Use the above-mentioned drugs in combination, and add weight loss drugs (such as orlistat, naltrexone / bupropion) if necessary. (3) Metabolic surgery: For individuals with MUO (muscular dystrophy) and a BMI > 35 kg / m² or BMI > 30 kg / m² with type 2 diabetes, metabolic surgery may be considered if lifestyle interventions and drug therapy are ineffective. Surgical procedures include: - Roux-en-Y gastric bypass surgery: best for weight loss and can significantly improve type 2 diabetes; - Sleeve gastrectomy: The surgery is relatively simple and has a low risk of complications; - Adjustable gastric banding: a reversible procedure, but with poor long-term results.

[0162] The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. The use of serum biomarker detection reagents in the preparation of products for obesity classification, obesity prognosis assessment, obesity management, and precision obesity treatment, among which, The serum biomarkers include serum cellobiose.

2. The use according to claim 1, wherein, 1) The obesity classification includes classifying obese individuals into metabolically healthy obesity (MHO) or metabolically unhealthy obesity (MUO) based on serum biomarker levels. For example, when the serum biomarker level is higher than the cutoff value, it is determined to be metabolically unhealthy obesity (MUO), while when it is lower than or equal to the cutoff value, it is determined to be metabolically healthy obesity (MHO). 2) The obesity prognostic assessment includes assessing obesity prognosis based on the obesity classification results; for example, for individuals with metabolically healthy obesity (MHO), assessing their risk of transitioning to metabolically unhealthy obesity (MUO); and / or for individuals with metabolically unhealthy obesity (MUO), assessing their risk of developing metabolic syndrome, impaired glucose tolerance, type 2 diabetes, progression of non-alcoholic fatty liver disease (NAFLD), or atherosclerotic cardiovascular disease (ASCVD); 3) The obesity management includes obesity management based on the obesity classification results; for example, for individuals with metabolically healthy obesity (MHO), a metabolic maintenance monitoring program is developed, including regular monitoring of the serum biomarker levels to detect the trend of conversion to metabolically unhealthy obesity (MUO) at an early stage; and / or for individuals with metabolically unhealthy obesity (MUO), an enhanced metabolic intervention program is developed, including dynamic evaluation of the intervention effect based on the serum biomarker levels. 4) The precision treatment of obesity includes selecting treatment strategies based on the obesity classification results; for example, for individuals with metabolically healthy obesity (MHO), a non-pharmacological treatment strategy with lifestyle intervention as the main approach is adopted; and / or for individuals with metabolically unhealthy obesity (MUO), a combination of drug treatment and lifestyle intervention is adopted, for example, the drug treatment includes insulin sensitizers, GLP-1 receptor agonists, SGLT2 inhibitors or metabolic surgery.

3. The use according to claim 2, wherein, The cutoff value is the value with the maximum Yoden index determined by receiver operating characteristic (ROC) analysis; and / or The cutoff value is 50-60 μmol / L; and / or The cutoff value is 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, or any value between any two numbers; and / or The cutoff value was 57.71 μmol / L.

4. The use according to claim 1, wherein, The serum biomarker is composed of or is a serum cellobiose; and / or The serum biomarkers also include other serum biomarkers, such as branched-chain amino acids (leucine, isoleucine, valine), glycolic acid, 4-hydroxypropionic acid, bile acid metabolites (primary bile acids, secondary bile acids), sphingolipids, phospholipids, palmitic acid, stearic acid, free fatty acids, malondialdehyde (MDA), carnitine, tryptophan metabolites, glutamine, glycine, serine; and / or The serum biomarkers consist of serum cellobiose and other serum biomarkers selected from at least one of the following groups: branched-chain amino acids (leucine, isoleucine, valine), glycolic acid, 4-hydroxypropionic acid, bile acid metabolites (primary bile acids, secondary bile acids), sphingolipids, phospholipids, palmitic acid, stearic acid, free fatty acids, malondialdehyde (MDA), carnitine, tryptophan metabolites, glutamine, glycine, and serine.

5. The use according to claim 1, wherein, The serum biomarker levels are obtained from the subject's fresh, refrigerated, or frozen serum; for example, the fresh serum is serum that was tested within 4 hours of collection without undergoing cryopreservation; for example, the serum is frozen serum, for example, serum that was collected, stored at -80°C, thawed, and then tested; and / or The serum biomarker levels were obtained from the subject's peritoneal serum or non-fasting serum; for example, the fasting serum was obtained by separating venous blood collected after the subject had fasted for more than 12 hours; and / or The serum cellobiose is α-cellobiose, β-cellobiose, or a combination thereof; and / or The objects are mammals, such as humans, non-human primates (e.g., orangutans, apes), rodents (e.g., rats, mice, guinea pigs), pets (e.g., cats, dogs), and livestock (e.g., horses, cattle, sheep, pigs, rabbits).

6. The use according to claim 1, wherein, The serum biomarker detection reagents include reagents for liquid chromatography-mass spectrometry (LC-MS / MS), gas chromatography-mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC), or electrochemical detection; and / or The serum biomarker detection reagents include reagents for detecting serum cellobiose levels, such as reagents for detecting serum cellobiose levels by liquid chromatography-mass spectrometry (LC-MS / MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography (HPLC), or electrochemical detection.

7. The use according to claim 1, wherein, The product is an in vitro diagnostic reagent kit, detection chip, detection card, detection system, or computer program product; and / or The in vitro diagnostic kit also includes at least one of the following: sample processing reagents, standards, quality control materials, diluents, washing solutions, or colorimetric solutions.

8. A kit for obesity classification, obesity prognostic assessment, obesity management, or precision obesity treatment, comprising: (i) A detection reagent for detecting serum biomarkers, wherein the serum biomarkers include the serum cellobiose of claim 1; (ii) Optionally, standards, such as serum biomarker standard solutions of varying concentrations; and / or other assay reagents for obesity genotyping; (iii) A specification describing a method for differentiating metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) by cutoff values, and clinical management recommendations based on this classification; and / or The cutoff value is 50-60 μmol / L, and / or the cutoff value is 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60 or any value between any two numbers, and / or the cutoff value is 57.71 μmol / L.

9. A system for obesity classification, obesity prognostic assessment, obesity management, or precision obesity treatment, wherein, The system includes: A data receiving module is used to receive or input level data of biomarkers in the serum of a subject, wherein the biomarkers include serum cellobiose as described in claim 1; The judgment module integrates an obesity classification model, which includes a cutoff value and compares serum cellobiose levels with the cutoff value; The output module is used to output the obesity metabolic classification result based on the comparison result of the judgment module; wherein, when the serum cellobiose level is greater than the cutoff value, the classification result of metabolically unhealthy obesity (MUO) is output, and optionally, suggestions for strengthening metabolic intervention are also output; when it is less than or equal to the cutoff value, the classification result of metabolically healthy obesity (MHO) is output, and optionally, suggestions for metabolic maintenance monitoring are also output.

10. The system according to claim 9, further comprising: A detection module for detecting the serum cellobiose level, for example, the detection module includes a liquid chromatography-tandem mass spectrometer configured with an electrospray ionization (ESI) source; and / or The data processing module is used to perform peak identification, internal standard calibration, and concentration calculation on the raw mass spectrometry data obtained by the detection module, generate the serum cellobiose level data, and transmit it to the data receiving module; and / or The output module is also used to output obesity prognostic assessment information, obesity management suggestions, or precision obesity treatment strategies based on the comparison results of the judgment module; for example... The obesity prognostic assessment information includes, for individuals with metabolically healthy obesity (MHO), a risk assessment of the transition to metabolically unhealthy obesity (MUO); and for individuals with metabolically unhealthy obesity (MUO), a risk assessment of the development of metabolic syndrome, impaired glucose tolerance, type 2 diabetes, progression of non-alcoholic fatty liver disease, or atherosclerotic cardiovascular disease. The clinical obesity management recommendations are as follows: For individuals with metabolically healthy obesity (MHO), a metabolic maintenance monitoring program is provided, including regular monitoring of serum cellobiose levels to detect early trends toward metabolically unhealthy obesity (MUO); for individuals with metabolically unhealthy obesity (MUO), an enhanced metabolic intervention program is provided, including dynamic evaluation of the intervention effect based on serum cellobiose levels. The precision treatment strategy for obesity includes a non-pharmacological treatment strategy with lifestyle intervention as the main component for individuals with metabolically healthy obesity (MHO); and a drug treatment combined with lifestyle intervention strategy for individuals with metabolically unhealthy obesity (MUO). The drug treatment includes insulin sensitizers, GLP-1 receptor agonists, SGLT2 inhibitors, or metabolic surgery.

Images