Use of 5-aminopentanamide in the preparation of a diagnostic product for osteoporosis

Abstract

The present application relates to the application of 5-amino pentanamide in the preparation of osteoporosis diagnostic products, belonging to the field of biotechnology. Non-targeted metabolomics analysis is carried out on the plasma exosomes of 100 postmenopausal osteoporosis patients (case group) and 100 healthy controls (control group), and further variable screening of differential metabolites is carried out by LASSO regression, and it is found that the regression coefficient absolute value of 5-amino pentanamide (5-Aminopentanamide) is the largest, which is the most critical metabolite for distinguishing the case group from the control group. The content of 5-amino pentanamide in the case group is significantly lower than that in the control group, and the area under the curve (AUC) is 0.903, which has good prediction effect. The diagnostic marker of the present application significantly improves the accuracy and specificity of postmenopausal osteoporosis diagnosis.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and in particular to the application of 5-aminopentanamide in the preparation of osteoporosis diagnostic products. Background Technology

[0002] Osteoporosis (OP) is a systemic metabolic bone disease characterized by decreased bone mass, destruction of bone microstructure, and increased bone fragility, significantly increasing the risk of fractures. Postmenopausal osteoporosis (PMOP) is a primary type of osteoporosis in women after menopause, caused by a sharp decline in estrogen levels, and is the most common type of osteoporosis.

[0003] The diagnosis of osteoporosis, especially postmenopausal osteoporosis, currently relies primarily on dual-energy X-ray absorptiometry (DXA) to measure bone mineral density (BMD) in the lumbar spine and hip, with a T-score ≤ -2.5 as the gold standard, combined with a history of fragility fractures for clinical evaluation. However, this standard has significant limitations: DXA only reflects the degree of bone mineralization and cannot assess bone microstructure, bone turnover rate, and bone quality. Many patients may have normal or slightly decreased BMD, but their bone quality has already deteriorated significantly, leading to the underdiagnosis of some patients with "occult osteoporosis"—normal BMD but decreased bone quality. Furthermore, over-reliance on T-scores also results in insufficient prediction of fracture risk in individuals with low bone mass. In addition, existing serum biomarkers (such as PINP and NTX) primarily reflect bone turnover rate, lack specificity, and are significantly affected by factors such as renal function and circadian rhythms. Therefore, developing novel, non-invasive, sensitive, and specific diagnostic biomarkers for osteoporosis, especially postmenopausal osteoporosis, has significant clinical value. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the problems of easy missed diagnosis and poor specificity in the diagnosis of osteoporosis, especially postmenopausal osteoporosis, in the prior art.

[0005] To address the aforementioned technical problems, this invention provides the application of 5-aminopentanamide in the preparation of diagnostic products for osteoporosis. This invention performed non-targeted metabolomics analysis on plasma exosomes from 100 postmenopausal osteoporosis patients (case group) and 100 healthy controls (control group), identifying a total of 135 differentially expressed metabolites. Of these, 79 metabolites were upregulated in the control group compared to the case group, and 56 metabolites were downregulated. Further LASSO regression analysis revealed that 5-aminopentanamide (CAS No. 13023-70-6) had the largest absolute regression coefficient, making it the most critical metabolite distinguishing between the case and control groups. The content of 5-aminopentanamide in the case group was significantly lower than that in the control group, with an area under the curve (AUC) of 0.903. In an independent validation cohort (100 postmenopausal osteoporosis patients (case validation group) vs 100 healthy controls (control validation group)), 5-aminopentamide also showed a significant downregulation trend with an AUC of 0.808, validating its diagnostic robustness.

[0006] The first objective of this invention is to provide an application of a reagent for detecting 5-aminopentanamide content in the preparation of a kit for the diagnosis of osteoporosis.

[0007] Furthermore, the osteoporosis includes postmenopausal osteoporosis.

[0008] Furthermore, the test sample for the kit is an exosome.

[0009] A second object of the present invention is to provide a diagnostic kit for osteoporosis, the kit containing reagents for detecting the content of 5-aminopentanamide.

[0010] Furthermore, the osteoporosis includes postmenopausal osteoporosis.

[0011] Furthermore, the diagnostic kit also includes reagents for extracting exosomes from plasma.

[0012] Furthermore, the reagents include those for detecting the concentration or content of 5-aminopentanamide in the sample by nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry, or a combination thereof.

[0013] A third objective of this invention is to provide the application of 5-aminopentanamide in the preparation of osteoporosis diagnostic products.

[0014] Furthermore, the diagnostic product includes equipment for analyzing the content of the 5-aminopentanamide.

[0015] The fourth objective of this invention is to provide an application of 5-aminopentanamide in constructing an osteoporosis prediction model.

[0016] Furthermore, the prediction model includes the step of analyzing the concentration level of 5-aminopentamide using one or more algorithms selected from orthogonal partial least squares discriminant analysis, partial least squares discriminant analysis, classification and logistic regression, k-nearest neighbor algorithm, Naive Bayes, support vector machine, decision tree, random forest, regression tree, gradient boosting decision tree, extreme gradient boosting algorithm, lightweight gradient boosting machine, gradient boosting machine, lasso algorithm and convolutional neural network.

[0017] Compared with the prior art, the above-described technical solution of the present invention has the following advantages:

[0018] This invention identifies 5-aminopentanamide as a metabolic marker for diagnosing osteoporosis, particularly postmenopausal osteoporosis. Specifically, the 5-aminopentanamide of this invention exhibits a high AUC of 0.903 in the diagnosis of postmenopausal osteoporosis, demonstrating excellent predictive ability. This diagnostic marker significantly improves the accuracy and specificity of diagnosing postmenopausal osteoporosis. Attached Figure Description

[0019] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0020] Figure 1 It is a volcano plot that discovers differentially metabolites in the cohort;

[0021] Figure 2 The results are shown in Figure A, which is the result of identifying core metabolic biomarkers using the LASSO model. Figure A shows the results of the λ value and the number of variables, where the bottom horizontal axis is log(λ value), the vertical axis is the 10-fold cross-validation error, and the top horizontal axis is the number of variables. Figure B is the ranking of important features selected based on the LASSO model. Features are arranged in descending order of the average reduction in Gini coefficient, with higher values ​​indicating greater importance of the corresponding feature in the model's classification decision.

[0022] Figure 3 This represents the expression level and diagnostic efficacy of 5-aminopentanamide in the discovery cohort; A is a box plot showing the relative content of 5-aminopentanamide in the case group and the control group (**** indicates...). P <0.0001); B is the ROC curve;

[0023] Figure 4 This represents the expression level and diagnostic efficacy of 5-aminopentanamide in the validation cohort; A is a box plot showing the relative content of 5-aminopentanamide in the case validation group and the control validation group (*** indicates...). P<0.001); B is the ROC curve. Detailed Implementation

[0024] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0025] Example 1: Screening of differentially metabolized plasma exosomes between postmenopausal osteoporosis patients and healthy controls

[0026] 1. Research subjects and research design

[0027] All participants obtained written informed consent before participating in the study. Venous blood was collected from participants in the morning on an empty stomach, plasma was separated, and stored at -80°C.

[0028] The study subjects (discovery cohort) in this embodiment consisted of two groups: (a) case group: 100 subjects with postmenopausal osteoporosis (PMOP); and (b) control group: healthy subjects matched for age and sex.

[0029] The inclusion and exclusion criteria for participants are as follows:

[0030] (1) Women aged ≥65 years were included;

[0031] (2) The inclusion criteria for osteoporosis were based on bone mineral density measured by dual-energy X-ray absorptiometry (T-value of femoral neck bone mineral density less than -2.5).

[0032] (3) Exclude patients with a history of fractures, liver and kidney dysfunction, thyroid dysfunction, chronic inflammation and infection, malignant tumors, etc.

[0033] 2. Isolation and identification of plasma exosomes

[0034] Exosomes in plasma samples were separated using a plasma exosome affinity extraction kit, and their size was calculated using nanoparticle tracking analysis. The morphology of the exosomes was then detected by transmission electron microscopy.

[0035] 3. Extraction of exosome metabolites

[0036] (1) Transfer the sample to a 2mL centrifuge tube and add 100mg of glass beads;

[0037] (2) Add 1.0 mL of a mixed solution of acetonitrile:methanol:H2O (2:2:1) and vortex for 30 s;

[0038] (3) Place the centrifuge tube in the instrument’s 2mL adapter, immerse it in liquid nitrogen for 5 minutes to freeze quickly, then thaw it at room temperature. Place the centrifuge tube in the 2mL adapter again, install it in the tissue homogenizer, and homogenize at 60Hz for 2 minutes. Repeat twice.

[0039] (4) Take out the centrifuge tube, process the sample at 12000 rpm and 4℃ for 10 min, collect the supernatant and then concentrate and dry it;

[0040] (5) The sample was reconstituted by adding 300µL of acetonitrile: 0.1% formic acid (1:9) to a 2-chloro-L-phenylalanine solution (2.5μg / mL), filtered through a 0.22μm membrane, and then detected by liquid chromatography-mass spectrometry (LC-MS).

[0041] 4. Metabolite LC-MS analysis

[0042] LC chromatographic conditions: An ACQUITYUPLC® HSST3 (2.1 × 100 mm, 1.8 µm) column was used, with a flow rate of 0.3 mL / min, a column temperature of 40 °C, and an injection volume of 2.0 μL. Positive ion mode was used, with a mobile phase of 0.1% formic acid acetonitrile (B1) and 0.1% formic acid water (A1). The gradient elution program was: 0–1 min, 8% B1; 1–8 min, 8%–98% B1; 8–10 min, 98% B1; 10–10.1 min, 98%–8% B1; 10.1–12 min, 8% B1. In negative ion mode, the mobile phase consisted of acetonitrile (B2) and 5 mM ammonium formate water (A2), with the gradient elution program as follows: 0-1 min, 8% B2; 1-8 min, 8%-98% B2; 8-10 min, 98% B2; 10-10.1 min, 98%-8% B2; 10.1-12 min, 8% B2.

[0043] MS mass spectrometry conditions: positive ion spray voltage 3.50 kV, negative ion spray voltage -2.50 kV, sheath gas 40 arb, auxiliary gas 10 arb. Capillary temperature 325℃. First-stage full scan at a resolution of 60,000 Hz, with a mass-to-charge ratio (m / z) range of 100-1000. Second-stage fragmentation was performed using high-energy collisional dissociation (HCD) at 30% collision energy, with a second-stage resolution of 15,000 Hz. The first four ions acquired were fragmented, and unnecessary tandem mass spectrometry (MS / MS) information was removed using dynamic exclusion.

[0044] 5. Data Preprocessing

[0045] (1) The raw LC-MS data were converted to mzXML format using the MSConvert tool in Proteo Wizard software, and peak identification, peak extraction, peak alignment, and integration were performed using the XCMS toolkit based on R language to obtain a list of substances for quantitative analysis. Support vector regression correction based on quality control samples was used to eliminate systematic errors. Then, substances with a coefficient of variation of less than 30% in the quality control samples were retained for subsequent analysis.

[0046] (2) Principal component analysis (PCA) was used to observe the overall distribution trend among samples. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to examine the differences between groups. The contribution of variables to the model was explained by the variable importance projection (VIP value), which can be used to measure the influence and explanatory power of the differences in the accumulation of each metabolite on the classification of each group of samples.

[0047] (3) The secondary mass spectra were compared and searched using the Kyoto Encyclopedia of Genes and Genomes (KEGG), the Lipid Metabolites and Pathway Strategies Database (LIPIDMaps), the Human Metabolome Database (HMDB), the Mass Spectrometry Database (MassBank), and the Mass Spectrometry Cloud Database (MzClou). The metabolites with secondary spectra in the quantitative list were compared and matched with the fragment ion information of each secondary spectra in the database to achieve secondary qualitative identification of the metabolites. A total of 350 metabolites were identified.

[0048] 6. Screening of metabolic biomarkers

[0049] (1) Perform a T-test to analyze the differences in metabolites and use the adjusted significance probability value (P) to determine the significance. FDR And VIP value for significant difference screening (P) FDR <0.05, VIP>1), a total of 135 differentially expressed metabolites were identified (see Figure 1 ).

[0050] (2) To reduce data dimensionality, prevent model overfitting, and identify the most biologically significant characteristic biomarkers, minimum absolute shrinkage and LASSO regression with 10-fold cross-validation were used for variable selection. The optimal λ value (λ = 0.005164646) was determined by minimizing the cross-validation bias, and 17 metabolites with non-zero coefficients were selected (see...). Figure 2 Among them, 5-aminopentanamide had the largest absolute value of regression coefficient (absolute value of regression coefficient = 1.47), indicating that it made the greatest contribution to distinguishing the case group from the control group (see...). Figure 2 B).

[0051] 7. Receiver Operating Characteristic (ROC) analysis

[0052] To further validate the specificity of 5-aminopentanamide in the diagnosis of postmenopausal osteoporosis, receiver operating characteristic (ROC) curve analysis was performed on the biomarker. The results are as follows: Figure 3 As shown, the area under the curve (AUC) is 0.903, indicating diagnostic value and demonstrating high accuracy and discriminative ability.

[0053] Example 2: Validation of differential plasma exosome metabolites between postmenopausal osteoporosis patients and healthy controls

[0054] 1. Verification of 5-aminopentanamide

[0055] This embodiment includes a validation cohort of 100 postmenopausal osteoporosis patients (case validation group) and 100 healthy controls (control validation group). Both postmenopausal osteoporosis patients and healthy controls were female and age-matched. Using the aforementioned detection and statistical analysis methods, the stable characteristic profile of 5-aminopentanamide was identified and validated in plasma exosomes. It reproduced the significant differences between the case validation group and the control validation group, and was consistent with the trend observed between the case group and the control group in Example 1. Figure 4 A).

[0056] 2. ROC analysis

[0057] ROC analysis results showed that 5-aminovalerate exhibited excellent specificity and sensitivity in predicting postmenopausal osteoporosis. Figure 4 As shown in Figure B, the area under the curve is as high as 0.808 (model: logit(P(Y=1)) = -0.5654 -2.8714×X, where X represents the standardized value of 5-aminopentanamide in the corresponding plasma exosomes measured by mass spectrometry). This indicates that 5-aminopentanamide can serve as a specific metabolic marker for postmenopausal osteoporosis and can be used for the diagnosis of postmenopausal osteoporosis.

[0058] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. Application of reagents for detecting 5-aminopentanamide content in the preparation of kits for the diagnosis of osteoporosis.

2. The application according to claim 1, characterized in that, The osteoporosis mentioned includes postmenopausal osteoporosis.

3. The application according to claim 1, characterized in that, The test sample for this kit is an exosome.

4. A diagnostic kit for osteoporosis, characterized in that, The diagnostic kit contains reagents for detecting the content of 5-aminopentanamide.

5. The diagnostic kit according to claim 4, characterized in that, The diagnostic kit also includes reagents for extracting exosomes from plasma.

6. The diagnostic kit according to claim 4, characterized in that, The reagents include those for detecting the concentration or content of 5-aminopentanamide in a sample by means of nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry, or a combination thereof. Application of 7,5-Aminopentanamide in the preparation of osteoporosis diagnostic products.

8. The application according to claim 7, characterized in that, The diagnostic product includes equipment for analyzing the content of the 5-aminopentanamide. Application of 9,5-Aminovalerate in constructing a predictive model for osteoporosis.

10. The application according to claim 9, characterized in that, The prediction model includes the steps of analyzing the concentration level of 5-aminopentamide using one or more of the following algorithms: orthogonal partial least squares discriminant analysis, partial least squares discriminant analysis, classification and logistic regression, k-nearest neighbor algorithm, Naive Bayes, support vector machine, decision tree, random forest, regression tree, gradient boosting decision tree, extreme gradient boosting algorithm, lightweight gradient boosting machine, gradient boosting machine, lasso algorithm, and convolutional neural network.

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