Biomarker MYD88 for evaluating visceral obesity and application thereof

Abstract

The invention discloses a biomarker MYD88 for evaluating visceral obesity and application of the biomarker MYD88, and belongs to the technical field of biomedical detection and molecular marker research. The marker is MYD88 in serum, the biomarker is serum protein MYD88, in the evaluation process, only a serum sample is needed, detection can be completed under conventional laboratory conditions based on serum protein detection, the specificity is high, operation is easy and convenient, cost is low, non-invasive performance is achieved, and the method is suitable for obesity typing screening and evaluation of large-scale people and has good application prospects. The method has the potential of wide popularization and application, provides a theoretical basis and a practical basis for early diagnosis and accurate intervention of visceral obesity, and effectively overcomes the limitation of an existing method.

Description

Technical Field

[0001] This invention relates to the field of biomedical detection and molecular marker research technology, specifically to a serum protein biomarker MYD88 for assessing visceral obesity and its applications. Background Technology

[0002] Obesity is a major public health problem worldwide, closely associated with the development of cardiovascular disease, type II diabetes, and various cancers. Clinically, based on the distribution of fat in the body, obesity is mainly divided into visceral obesity and subcutaneous obesity, with significant differences between the two in terms of metabolic characteristics and health risks.

[0003] Visceral adipose tissue is mainly deposited in the abdominal cavity, surrounding vital organs such as the liver, pancreas, and intestines. It has a high fat mobilization capacity and endocrine activity, secreting various inflammatory factors and metabolic regulatory molecules, thus significantly increasing the risk of metabolic disorders and related diseases. In contrast, subcutaneous adipose tissue is mainly distributed under the skin, concentrated in the thigh and buttock areas. Its metabolic activity and inflammatory factor secretion levels are relatively low, and it has a smaller impact on the body's metabolic homeostasis.

[0004] Therefore, compared with subcutaneous adipose tissue (SAT) obesity, visceral adipose tissue (VAT) obesity poses a more significant risk in terms of metabolic abnormalities, cardiovascular disease, and cancer risk. Effective identification and assessment of VAT obesity has become an important need in clinical diagnosis and disease prevention.

[0005] Currently, Body Mass Index (BMI), Waist-to-Hip Ratio (WHR) (PMID: 39313919), and imaging examinations (such as computed tomography (CT, PMID: 34839215) and magnetic resonance imaging (MRI, PMID: 36918706)) are commonly used indicators for obesity assessment. However, these methods have significant limitations: BMI cannot distinguish between the ratio of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT); the waist-to-hip ratio is easily affected by individual differences, resulting in insufficient accuracy; CT / MRI is costly and complex to operate, making it unsuitable for large-scale screening. Meanwhile, although existing metabolomics and proteomics studies have identified some obesity-related molecules, they lack specific biomarkers for visceral obesity, limiting their clinical application value; while imaging examinations can accurately assess fat distribution, they are expensive, require sophisticated equipment, and are complex to operate, making them unsuitable for large-scale screening. In recent years, some studies have attempted to use metabolomics or proteomics to find serological biomarkers associated with obesity. However, most of these studies have focused on people with overall obesity or type II diabetes, and there is a lack of exploration for biomarkers specific to visceral obesity.

[0006] Therefore, there is an urgent need to develop a non-invasive, high-throughput, cost-effective, and convenient detection method for the early identification of obesity subtypes, especially visceral obesity, which carries a higher metabolic risk. Summary of the Invention

[0007] To address the aforementioned problems, this invention proposes a biomarker, MYD88, for assessing visceral obesity and its applications.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solution: A biomarker for assessing visceral obesity, wherein the biomarker is any one or more of serum protein MYD88, serum protein TSC22D1, and serum protein PZP.

[0009] This invention also discloses the use of biomarkers serum protein MYD88, or serum protein TSC22D1, or serum protein PZP in the preparation of products for evaluating visceral obesity or for preparing products for regulating visceral obesity.

[0010] Furthermore, the product for assessing visceral obesity includes a detection reagent or test kit or diagnostic device, and the product assesses visceral obesity by detecting the expression levels of any one or more of serum proteins MYD88, TSC22D1, and PZP in the sample.

[0011] Furthermore, the test sample is peripheral serum.

[0012] Furthermore, the expression level of serum protein MYD88 in the test sample is positively correlated with the assessment of visceral obesity; the AUC value of serum protein MYD88 in the test sample is not less than 0.9.

[0013] Furthermore, the expression level of serum protein TSC22D1 in the test sample is positively correlated with the assessment of visceral obesity; the AUC value of serum protein TSC22D1 in the test sample is not less than 0.9.

[0014] Furthermore, the expression level of serum protein PZP in the tested samples was positively correlated with the assessment of visceral obesity, and the AUC value of serum protein PZP in the tested samples was not less than 0.9. This invention also discloses the application of a product containing the biomarker MYD88 for assessing visceral obesity, for non-diagnostic purposes, including... (1) An analysis module, wherein the analysis module is used to determine the expression level of biomarkers in the test sample of the subject, and; (2) Assessment module, which is used to determine whether the subject has visceral obesity based on the expression level of the marker determined in (1); Among them, the biomarkers mentioned in (1) are serum protein MYD88, serum protein TSC22D1, or serum protein PZP in peripheral serum.

[0015] Furthermore, the assessment module determines that visceral obesity is positively correlated with the levels of serum protein MYD88, TSC22D1, or PZP in peripheral blood.

[0016] This invention relates to MYD88, a biomarker for evaluating visceral obesity, and its applications. Its beneficial effects include: (1) The biomarker serum protein MYD88 of the present invention can be used as a non-invasive biomarker for early diagnosis and precise intervention of visceral obesity. In addition, it provides a theoretical basis for the study of obesity-related metabolic abnormality mechanisms and potential intervention targets.

[0017] (2) The biomarker of this invention is serum protein. In the evaluation process, this invention only requires serum samples. Based on serum protein detection, the detection can be completed under routine laboratory conditions. It is simple to operate, low in cost and non-invasive. It is suitable for large-scale population obesity classification screening and evaluation. It has the potential for widespread application and provides a theoretical basis and practical foundation for the early diagnosis and precise intervention of visceral obesity. It effectively overcomes the limitations of existing methods.

[0018] (3) High specificity: This invention proposes for the first time that serum protein MYD88 is a potential biomarker for distinguishing between visceral obesity and subcutaneous obesity, filling the gap in clinical testing.

[0019] (4) Great potential for clinical application: Through ROC analysis, the serum protein MYD88 screened in this invention showed high sensitivity and specificity (AUC>0.85) in distinguishing between visceral obesity and subcutaneous obesity, showing good potential for clinical application. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1This study involves differential serum protein analysis and machine learning between visceral and subcutaneous obesity groups. A. Volcano plot showing differentially expressed proteins between the visceral and subcutaneous obesity groups (screening criteria: |log2FC|>1 and p<0.05); B. KEGG pathway enrichment results for differentially expressed proteins; C. Importance assessment results of 201 differentially expressed proteins using the RF model; D. LASSO regression analysis results. Figure 2 This is a Spearman correlation analysis of candidate proteins and obesity-related clinical indicators. Red indicates a positive correlation, blue indicates a negative correlation, and significance levels are marked with asterisks: * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001; Figure 3 This is a correlation analysis between candidate proteins and the VAT / SAT ratio. (A–L) are scatter plots showing the correlation between 12 candidate proteins (MYD88, TSC22D1, GKN2, CTNNA1, MB, OSBPL9, RMDN1, RPS23, SFT2D3, PSMA7, PZP, and HNRNPR) and the VAT / SAT ratio. Spearman correlation coefficients (R and p-values) are labeled in the figures. Figure 4 The ROC curves of candidate proteins based on DIA proteomics analysis in distinguishing between visceral and subcutaneous obesity are shown. Figure 5 This section presents subcellular localization and Mantel test analyses of candidate proteins in obesity-related pathways. A shows the subcellular localization of six candidate proteins (MYD88, TSC22D1, GKN2, CTNNA1, PSMA7, and PZP) predicted based on the GeneCards database. B shows the Mantel correlation analysis between the candidate proteins and proteins in the KEGG_PPAR_SIGNALING_PATHWAY database. C shows the Mantel correlation analysis between the candidate proteins and proteins in the KEGG_INSULIN_SIGNALING_PATHWAY database. Figure 6 To validate the expression levels and diagnostic value of candidate serum proteins in visceral and subcutaneous obesity using ELISA; A shows the ELISA quantitative results of serum protein MYD88 in the visceral obesity group (n=9) and the subcutaneous obesity group (n=9), with differences between groups assessed by t-test; B shows the ROC curve analysis of serum protein MYD88, demonstrating its diagnostic efficacy in distinguishing between visceral and subcutaneous obesity. Significance levels are indicated by asterisks: * indicates p<0.05, ** indicates p<0.01, *** indicates p<0.001. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solution of the present invention will be clearly and completely described below with reference to embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] A biomarker for assessing visceral obesity is any one or more of serum proteins MYD88, TSC22D1, and PZP in peripheral serum.

[0024] The biomarkers of this invention can be used to prepare products for assessing visceral obesity, such as visceral obesity detection kits.

[0025] Example 1

[0026] The detection method for visceral obesity based on serum proteins includes the following steps: I. Serum proteomics detection Serum proteins were quantitatively detected in individuals with visceral obesity (VAT / SAT>0.9, 8 cases) and subcutaneous obesity (VAT / SAT<0.5, 4 cases) using data-independent acquisition (DIA) mass spectrometry, and serum protein expression profiles were obtained.

[0027] II. Screening of serum protein biomarkers (1) Plasma sample pretreatment and extraction of metabolite molecules Sample pretreatment using analytical grade reagents: ① Take out the plasma sample stored at -80℃, thaw it on ice, take 100μL of plasma into a 1.5mL centrifuge tube for extracting metabolite molecules, add standardized L-2-chlorophenylalanine as an internal reference metabolite (final concentration 0.3 mg / mL; prepared with 10μL methanol), and shake for 10 sec to mix. ② Organic solvent precipitation of proteins: Pre-cool all reagents at -20℃, add protein precipitant (methanol:acetonitrile 200μL:100μL), shake for 1 min; 0℃ water bath, sonicate for 10 min, freeze at -20℃ for 30 min; centrifuge at 13000 rpm, 4℃ for 10 min, the supernatant is the small molecule metabolite, the precipitate is the large molecule protein and cell debris, etc. Take 200μL of supernatant, evaporate and dry at -20℃ to obtain the metabolic molecules; ③ Redissolution and re-extraction: The metabolic molecules obtained in step ② were redissolved in 300 μL of methanol-water (1:4), vortexed for 30 s, sonicated for 3 min, frozen at -20℃ for 2 hours, centrifuged at 13000 rpm and 4℃ for 10 min, and 150 μL of the supernatant was filtered through a 0.22 μm pinhole filter to obtain the metabolite extract, which was stored at -80℃.

[0028] ④ Preparation of quality control samples (QC): Take equal volumes of metabolite extracts from all samples and mix them to prepare QC samples. The total volume of the QC samples is the same as that of the samples to be tested.

[0029] (2) Biomarker screening Bioinformatics methods were used to screen characteristic proteins in the test samples through differential analysis, random forest (RF) and LASSO regression. Spearman correlation analysis was performed on the candidate proteins and obesity-related clinical indicators, including BMI, WHR, VAT / SAT ratio, and blood lipid indicators (such as TG), combined with receiver operating characteristic (ROC) curve analysis, to screen out biomarkers: TSC22D1, MYD88, and PZP (the AUC values ​​of these three proteins are all >0.9). Figure 1 , Figure 2 and Figure 3 As shown, the specific filtering method is as follows: ① Differential analysis of protein expression profiles using the limma package identified 201 significantly differentially expressed proteins (p < 0.05). These were selected as core differentially expressed proteins with potential diagnostic value. Machine learning methods were then applied to feature selection from these 201 differentially expressed proteins. First, a RF model based on 5-fold cross-validation was constructed to evaluate the contribution of each protein to classification performance. Proteins were ranked according to their importance based on Gini coefficients, and the top 30 proteins with the highest Gini coefficients were selected as preliminary candidate features. Next, to further compress feature dimensions and identify the most predictive variables, LASSO regression analysis was performed on these 30 proteins. The optimal regularization parameter λ was determined using 5-fold cross-validation, ultimately identifying three key proteins: PSMA7, CTNNA1, and GKN2.

[0030] ② Considering the top 10 proteins in terms of Gini coefficient (PZP, HNRNPR, MB, OSBPL9, RMDN1, RPS23, MYD88, SFT2D3, TSC22D1, GKN2) and the LASSO screening results, a total of 12 candidate proteins were included for subsequent analysis. The ROC curve, with false positive rate (FPR) on the x-axis and true positive rate (TPR) on the y-axis, describes the model's classification ability at different decision thresholds. The area under the curve (AUC) quantifies the overall discriminative ability of the candidate proteins.

[0031] III. Investigation of Serum Detectability and Function of Candidate Proteins Serum detectability and functional analysis were performed on the screened candidate proteins (MYD88, TSC22D1, PZP). Results are as follows: Figure 4 and Figure 5 As shown, subcellular localization analysis revealed that GKN2 and PZP were mainly located extracellularly, while CTNNA1, MYD88, and PSMA7 were mainly distributed in the cytoplasm, plasma membrane, and cytoskeleton. TSC22D1 was distributed in the nucleus, cytoplasm, and mitochondria, providing a structural basis for serum detection. Single-gene enrichment analysis and Mantel test results indicated that these candidate proteins are closely related to lipid metabolism, insulin signaling, and chronic inflammation-related pathways, suggesting that they may play a regulatory role in the metabolic differences between visceral and subcutaneous obesity. The above analysis, based on subcellular localization and biological function, supports the rationale for the detectability of candidate proteins in serum and provides a theoretical basis for their potential as biomarkers for differentiating obesity phenotypes.

[0032] IV. ELISA Validation To verify the reliability of the three candidate proteins (TSC22D1, MYD88, and PZP) screened through bioinformatics analysis as potential biomarkers for visceral obesity, enzyme-linked immunosorbent assay (ELISA) was performed on the three candidate proteins.

[0033] The specific method was as follows: ELISA was performed on 9 serum samples from each of the visceral obesity group and the subcutaneous obesity group. Each sample was subjected to three biological replicates, and the average of the replicate results was taken as the protein expression level of that sample.

[0034] like Figure 6 As shown, the results indicated that MYD88 expression was significantly higher in the visceral obesity group than in the subcutaneous obesity group, and ROC analysis showed AUC>0.8, consistent with the DIA proteomics results, further supporting the reliability and potential clinical value of serum protein MYD88 in the diagnosis of visceral obesity.

[0035] Example 2

[0036] A kit for assessing visceral obesity includes a serum protein MYD88 and reagents for isolating nucleic acids from a sample. The serum protein MYD88 is detectably labeled.

[0037] Three serum samples were selected from each of the visceral obesity group and the subcutaneous obesity group. The expression level of serum protein MYD88 in the serum samples was detected using the kit of this invention. The expression level of serum protein MYD88 was positively correlated with visceral obesity.

[0038] The visceral obesity detected by the kit of the present invention was retested by ELISA, and the results were the same as those detected by the kit of the present invention.

[0039] Example 3

[0040] A kit for assessing visceral obesity includes serum protein PZP and reagents for isolating nucleic acids from samples. Serum protein PZP is detectably labeled.

[0041] Three serum samples were selected from each of the visceral obesity group and the subcutaneous obesity group. The expression level of serum protein PZP in the serum samples was detected using the kit of this invention. The expression level of serum protein PZP was positively correlated with visceral obesity.

[0042] The visceral obesity detected by the kit of the present invention was retested by ELISA, and the results were the same as those detected by the kit of the present invention.

[0043] Example 4

[0044] A kit for assessing visceral obesity includes serum protein TSC22D1 and reagents for isolating nucleic acids from samples. Serum protein TSC22D1 is detectably labeled.

[0045] Three serum samples were selected from each of the visceral obesity group and the subcutaneous obesity group. The expression level of serum protein TSC22D1 in the serum samples was detected using the kit of this invention. The expression level of serum protein TSC22D1 was positively correlated with visceral obesity.

[0046] The visceral obesity detected by the kit of the present invention was retested by ELISA, and the results were the same as those detected by the kit of the present invention.

[0047] Example 5

[0048] A kit for assessing visceral obesity includes serum protein MYD88, serum protein TSC22D1, and reagents for isolating nucleic acids from samples. Serum proteins MYD88 and TSC22D1 are detectably labeled.

[0049] Three serum samples were selected from each of the visceral obesity group and the subcutaneous obesity group. The kit of this invention was used to detect the expression levels of serum protein MYD88 and serum protein TSC22D1 in the serum samples. The expression levels of serum protein MYD88 and serum protein TSC22D1 were positively correlated with visceral obesity.

[0050] The visceral obesity detected by the kit of the present invention was retested by ELISA, and the results were the same as those detected by the kit of the present invention.

[0051] Example 6

[0052] A kit for assessing visceral obesity includes serum proteins MYD88, TSC22D1, and PZP, as well as reagents for isolating nucleic acids from samples. Serum proteins MYD88, TSC22D1, and PZP are detectably labeled.

[0053] Three serum samples were selected from each of the visceral obesity group and the subcutaneous obesity group. The kit of this invention was used to detect the expression levels of serum protein MYD88, serum protein TSC22D1 and serum protein PZP in the serum samples. The expression levels of serum protein MYD88, serum protein TSC22D1 and serum protein PZP were positively correlated with visceral obesity.

[0054] The visceral obesity detected by the kit of the present invention was retested by ELISA, and the results were the same as those detected by the kit of the present invention.

[0055] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0056] Finally, it should be noted that the embodiments disclosed in this invention are merely preferred embodiments of this invention and are only used to illustrate the technical solutions of this invention, not to limit it. Although this invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this invention.

Claims

1. A biomarker for assessing visceral obesity, characterized in that: The biomarker is any one or more of serum protein MYD88, serum protein TSC22D1, and serum protein PZP.

2. The use of a biomarker according to claim 1 in the preparation of products for evaluating visceral obesity.

3. The application of the biomarker according to claim 2 in the preparation and evaluation of products for visceral obesity, characterized in that: The product for assessing visceral obesity includes a test reagent or test kit or diagnostic device, and the product assesses visceral obesity by detecting the expression levels of any one or more of serum proteins MYD88, TSC22D1 and PZP in a sample.

4. The application of the biomarker according to claim 3 in the preparation and evaluation of products for visceral obesity, characterized in that: The test sample was peripheral serum.

5. The application of the biomarker according to claim 4 in the preparation and evaluation of products for visceral obesity, characterized in that: The expression level of serum protein MYD88 in the tested samples was positively correlated with the assessment of visceral obesity.

6. The application of the biomarker according to claim 5 in the preparation and evaluation of products for visceral obesity, characterized in that: The AUC value of serum protein MYD88 in the test sample is not less than 0.

9.

7. The application of the biomarker according to claim 4 in the preparation and evaluation of products for visceral obesity, characterized in that: The expression level of serum protein TSC22D1 in the test samples was positively correlated with the assessment of visceral obesity; the AUC value of serum protein TSC22D1 in the test samples was not less than 0.

9.

8. The application of the biomarker according to claim 4 in the preparation and evaluation of products for visceral obesity, characterized in that: The expression level of serum protein PZP in the test samples was positively correlated with the assessment of visceral obesity, and the AUC value of serum protein PZP in the test samples was not less than 0.9.

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