Use of biomarkers in predicting the effect of intensive treatment of diabetes, kit

Abstract

This invention discloses the application of a biomarker in predicting the efficacy of intensive insulin therapy for diabetes, a kit for predicting the efficacy of intensive insulin therapy for diabetes, and a method for using the kit. The kit can effectively predict the efficacy of intensive insulin therapy for diabetes, exhibiting high efficiency and specificity in predicting the efficacy of intensive insulin therapy in newly diagnosed diabetic patients, thus solving the problem of predicting differences in the efficacy of intensive insulin therapy. The biomarker used in predicting the efficacy of intensive insulin therapy for diabetes includes: 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine, and 3-methylhistidine.

Description

Technical Field

[0001] This invention relates to the field of biomedical detection technology, and more particularly to the application of a biomarker in predicting the efficacy of intensive diabetes treatment, a kit for predicting the efficacy of intensive diabetes treatment, and a method for using the kit. Background Technology

[0002] Diabetes mellitus is a prevalent chronic disease worldwide and a significant risk factor for cardiovascular and cerebrovascular diseases. Therefore, early identification and standardized treatment of diabetes are crucial for reducing disease mortality and improving prognosis. Short-term intensive insulin therapy is an important treatment for newly diagnosed diabetes patients, enabling a considerable number to control their blood sugar within the ideal range through diet and appropriate exercise alone. However, individual differences can still affect prognosis. Current predictive methods do not differentiate between treatment drugs and dosage forms, lacking a method for predicting the efficacy of intensive insulin therapy, a vital treatment approach. There is an urgent need to find a method that uses specific biomarkers to reflect the efficacy of intensive insulin therapy in patients.

[0003] Fatty acid metabolism disorders refer to abnormalities in the production, breakdown, transport, and utilization of fatty acids in the body. These abnormalities lead to the accumulation of fatty acids in the body, thereby triggering various metabolic problems. Excessive intake or abnormal metabolism of fatty acids can impair insulin signaling, resulting in reduced glucose uptake and utilization, promoting the development of hyperglycemia, and causing insulin resistance. Relief of insulin resistance is a crucial prerequisite for the effectiveness of intensive insulin therapy. Current methods for predicting the efficacy of diabetes treatment focus on various treatment modalities for all diabetic patients, and the predictive effect on the critical early stage of diabetes and the important means of intensive therapy remains unknown. Therefore, performing metabolomics analysis on the serum of newly diagnosed diabetic patients using liquid chromatography-mass spectrometry (LC-MS) to screen and analyze biomarkers with good sensitivity and specificity in predicting the efficacy of intensive therapy for diabetes can fill the gap in predictive indicators for the efficacy of intensive therapy in newly diagnosed diabetic patients. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, the technical problem to be solved by the present invention is to provide an application of a biomarker in predicting the effect of intensive treatment for diabetes, which can effectively predict the effect of intensive treatment for diabetes, and has high efficiency and specificity in predicting the efficacy of intensive insulin treatment for newly diagnosed diabetic patients, thus solving the problem of predicting the difference in efficacy of intensive insulin treatment.

[0005] The technical solution of the present invention is: the application of this biomarker in predicting the effect of intensive treatment for diabetes, wherein the biomarker includes: 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine.

[0006] The beneficial effects of this invention are: it can effectively predict the effect of intensive insulin therapy for diabetes, and has high efficiency and specificity in predicting the efficacy of intensive insulin therapy for newly diagnosed diabetic patients, thus solving the problem of predicting differences in the efficacy of intensive insulin therapy.

[0007] Kits for predicting the effectiveness of intensive diabetes treatment are also provided, which include internal standards for detecting 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine, and 3-methylhistidine.

[0008] The kit for predicting the effectiveness of intensive diabetes treatment is also provided, comprising the following steps: placing the sample to be tested in chelating agent EDTA, centrifuging, and then mixing 100 μL with 400 μL of protein removal agent; centrifuging again and drying the supernatant in a vacuum centrifuge; redissolving in acetonitrile solution with water as solvent; and performing liquid chromatography-mass spectrometry (LC-MS) to detect the contents of 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine, and 3-methylhistidine. Attached Figure Description

[0009] Figure 1 A is a schematic diagram (positive ion mode) of principal component (PCA) analysis of non-targeted metabolome data of newly diagnosed diabetic patients in the insulin intensive treatment remission group and the non-remission group; Figure 1 B is a schematic diagram of principal component (PCA) analysis of non-targeted metabolome data (negative ion mode) for newly diagnosed diabetic patients in the insulin intensive treatment remission group and the non-remission group.

[0010] Figure 2 The percentage of metabolites identified by non-target metabolomics in each chemical category.

[0011] Figure 3 A represents the differential metabolite volcano plot (positive ion mode) between the remission group and the non-remission group; Figure 3 B is a volcano diagram (negative ion pattern) showing the difference in metabolites between the remission group and the non-remission group.

[0012] Figure 4 A shows the differential metabolite heatmap (positive ion mode) between the remission group and the non-remission group; Figure 4 B is a heatmap of differential metabolites (negative ion pattern) between the remission group and the non-remission group.

[0013] Figure 5 A is a radar chart showing the fold change in differential metabolites between the remission group and the non-remission group; Figure 5 B is a radar chart showing the fold change in metabolites between the remission group and the non-remission group.

[0014] Figure 6 Bar graph showing the fold differences in four fatty acid metabolites.

[0015] Figure 7 The ROC analysis results of 3-hydroxybutyrylcarnitine in the relief group and the non-relief group are shown.

[0016] Figure 8 The ROC analysis results for 3-methylhistidine in the relieved and non-relieved groups are shown.

[0017] Figure 9 The ROC analysis results of 3-hydroxyhexadecylcarnitine in the relief group and the non-relief group are shown.

[0018] Figure 10 The ROC analysis results of 3-methylglutarylcarnitine in the relief group and the non-relief group are shown.

[0019] Figure 11 The ROC analysis results are for the remission group and the non-remission group.

[0020] Figure 12 For the detection of HepG2 cell viability.

[0021] Figure 13 To detect glucose content in the supernatant of HepG2 cells.

[0022] Figure 14 Western blot images of HepG2 cells and their quantitative analysis. Detailed Implementation

[0023] The application of this biomarker in predicting the effectiveness of intensive diabetes treatment includes: 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine, and 3-methylhistidine.

[0024] The beneficial effects of this invention are: it can effectively predict the effect of intensive insulin therapy for diabetes, and has high efficiency and specificity in predicting the efficacy of intensive insulin therapy for newly diagnosed diabetic patients, thus solving the problem of predicting differences in the efficacy of intensive insulin therapy.

[0025] Preferably, the diabetes intensive treatment is a 2-week intensive insulin treatment for newly diagnosed diabetic patients.

[0026] Kits for predicting the effectiveness of intensive diabetes treatment are also provided, which include internal standards for detecting 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine, and 3-methylhistidine.

[0027] The kit for predicting the effectiveness of intensive diabetes treatment is also provided, comprising the following steps: placing the sample to be tested in chelating agent EDTA, centrifuging, and then mixing 100 μL with 400 μL of protein removal agent; centrifuging again and drying the supernatant in a vacuum centrifuge; redissolving in acetonitrile solution with water as solvent; and performing liquid chromatography-mass spectrometry (LC-MS) to detect the contents of 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine, and 3-methylhistidine.

[0028] Preferably, the protein removal agent is cold methanol and acetonitrile.

[0029] Preferably, the mixing ratio of the cold methanol and acetonitrile solution is 1:1.

[0030] Preferably, the sample to be tested is serum.

[0031] Preferably, the acetonitrile solution with water as the solvent has a water-to-acetonitrile mixing ratio of 1:1 and a volume of 100 μl.

[0032] Preferably, in LC-MS detection, for hydrophilic exchange chromatography separation, the sample is analyzed using a 2.1 mm × 100 mm ACQUIY UPLC BEH 1.7 μm column. In ESI positive and negative ion mode, mobile phase A is an aqueous solution containing 25 mM ammonium acetate and 25 mM ammonium hydroxide, and mobile phase B is acetonitrile. The gradient program is as follows: initially B is 85%, linearly reduced to 65% after 1 minute, then reduced to 40% in 0.1 minutes, held for 4 minutes, then increased to 85% in 0.1 minutes, and re-equilibrated for 5 minutes.

[0033] Preferably, the method further includes:

[0034] For RPLC separation, a 2.1 mm × 100 mm ACQUIYUPLC HSS T3 1.8 μm column was used. In ESI positive ion mode, mobile phase A was water containing 0.1% formic acid, and mobile phase B was acetonitrile containing 0.1% formic acid. In ESI negative ion mode, mobile phase A was water containing 0.5 mm ammonium fluoride, and mobile phase B was acetonitrile. The gradient program was as follows: an initial 1% solution B was linearly increased to 99% over 1.5 min and held for 11.5 min, then decreased to 1% over 0.1 min, followed by a 3.4 min reequilibrium period. The gradient flow rate was 0.3 mL / min, and the column temperature was kept constant at 25 °C. 2 μL of each sample was injected.

[0035] The ESI source conditions are set as follows: ion source gas 1 is set to 60, ion source gas 2 is set to 60, curtain gas is set to 30, source temperature is 600℃, and IonSpray voltage fluctuation is set to ±5500V. In single-mode mass spectrometry, the instrument is set to acquire data in the m / z range of 60-1000Da, and the cumulative time for TOF mass spectrometry is set to 0.20s / spectrum. In automatic MS / MS acquisition mode, the instrument is set to acquire data in the m / z range of 25-1000Da, and the cumulative time for product ion scanning is set to 0.05s / spectrum. Product ion scanning uses information-dependent acquisition, selects high-sensitivity mode, and sets the following parameters: collision energy is fixed at 35V, segregation potential is 60V and -60V, isotopes in the 4Da range are excluded, and the number of candidate ions monitored per cycle is 10.

[0036] Information processing: Raw mass spectrometry data were converted to MzXML files using Proteo Wizard MSConvert before being imported into XCMS software. During peak identification, the following parameters were used: centWave m / z = 10 ppm, peak width = c(10,60), pre-filter = c(10,100). For peak grouping, bw = 5, mzwid = 0.025, and minfrac = 0.5 were used. The CAMERA algorithm set for metabolite profiling was used for isotope and adduct annotation. Among the extracted ion features, only variables with more than 50% non-zero measurements in at least one group were retained. Metabolite compound identification was performed by comparing accuracy m / z values ​​<10 ppm and MS / MS spectra with an established internal database containing real standards.

[0037] Statistical analysis: After data normalization, data analysis was performed using R packages, including Pareto-scaled principal component analysis (PCA). The robustness of the model was assessed through 7-fold cross-validation and response permutation tests. The projected importance (VIP) value of each variable was calculated to indicate its contribution to the classification. Student's t-test was applied to determine the significance of differences between two groups in the independent samples. Metabolites with significant changes were screened using VIP > 1 and p-value < 0.05. Pearson correlation analysis was performed to determine the correlation between two variables.

[0038] The embodiments of the present invention will be described in detail below.

[0039] Example 1

[0040] A kit for predicting the efficacy of intensive treatment in newly diagnosed diabetic patients includes: internal standards for detecting 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine, methanol, acetonitrile and purified water, etc.

[0041] The levels of 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine, and 3-methylhistidine in serum samples were detected by LC-MS, including the following steps:

[0042] (1) Inclusion criteria: Subjects were diagnosed with diabetes according to the WHO 1999 diagnostic criteria, with a disease duration of 0-5 years. Exclusion criteria: Type 1 diabetes, ketoacidosis, stress conditions such as infection, and exclusion of other autoimmune diseases and liver and kidney diseases. Enrolled subjects completed baseline general information and blood glucose, blood lipid, insulin, and glycated hemoglobin (HbA1c) tests. Intensive treatment was then administered via insulin pump or multiple subcutaneous insulin injections. Treatment goals were defined as fasting plasma glucose (FPG) <6.5 mmol / L and 2-hour postprandial glucose (2hPG) <8.0 mmol / L. After achieving these goals, maintenance treatment was continued for 2 weeks, followed by a repeat baseline examination 24 hours after insulin discontinuation. After discharge, subjects continued dietary control, appropriate exercise, and blood glucose monitoring. HbA1c and blood glucose were rechecked 3 months after treatment cessation. FPG <7.0 mmol / L and 2hPG <10 mmol / L were considered in remission; otherwise, no remission was indicated.

[0043] (2) Take 5 mL of vacuum aspiration tube containing EDTA and draw 3 mL of blood from the subject's elbow vein after fasting for 8 hours. Centrifuge for 15 min (1500 g, 4 °C). Store each aliquot (150 μL) of plasma sample at -80 °C until UPLC-Q-TOF / MS analysis.

[0044] (3) The plasma sample was thawed at 4°C, and 100 μL of aliquots were mixed with 400 μL of cold methanol / acetonitrile (1:1, v / v) to remove proteins. The mixture was centrifuged for 15 min (14000 g, 4°C). The supernatant was dried in a vacuum centrifuge.

[0045] (4) In LC-mass spectrometry analysis, the sample was redissolved in 100 μL of acetonitrile / water (1:1, v / v) solvent.

[0046] (5) LC-MS Analysis: Detection was performed using an ultra-high performance liquid chromatograph (1290Infinity LC, Agilent Technologies) with a quadrupole time-of-flight mass spectrometer (AB Sciex TripleTOF 6600). For hydrophilic exchange chromatography separation, samples were analyzed using a 2.1 mm × 100 mm ACQUIY UPLCBEH 1.7 μm column (Waters, Ireland). In ESI positive and negative ion mode, mobile phase A was an aqueous solution containing 25 mM ammonium acetate and 25 mM ammonium hydroxide, and mobile phase B was acetonitrile. The gradient program was as follows: initially B was 85%, linearly decreased to 65% after 1 minute (within 11 minutes), then decreased to 40% in 0.1 minutes, held for 4 minutes, then increased to 85% in 0.1 minutes, and reequilibrated for 5 minutes.

[0047] (6) For reversed-phase liquid chromatography (RPLC) separation, an ACQUIY UPLC HSS T3 1.8 μm column (Waters, Ireland) with dimensions of 2.1 mm × 100 mm was used. In ESI positive ion mode, mobile phase A was water containing 0.1% formic acid, and mobile phase B was acetonitrile containing 0.1% formic acid; in ESI negative ion mode, mobile phase A was water containing 0.5 mm ammonium fluoride, and mobile phase B was acetonitrile. The gradient program was as follows: an initial 1% solution B was linearly increased to 99% over 1.5 min and held for 11.5 min, then decreased to 1% over 0.1 min, followed by a 3.4 min reequilibrium period. The gradient flow rate was 0.3 mL / min, and the column temperature was kept constant at 25 °C. 2 μL of each sample was injected. (7) The ESI source conditions were set as follows: Ion source gas 1 (Gas1) was set to 60, ion source gas 2 (Gas2) was set to 60, curtain gas (CUR) was set to 30, source temperature was 600℃, and IonSpray voltage fluctuation (ISVF) was set to ±5500V. In single-mode mass spectrometry (MS), the instrument was set to acquire data in the m / z range of 60-1000Da, and the cumulative time for TOF mass spectrometry was set to 0.20 s / spectrum. In automatic MS / MS acquisition mode, the instrument was set to acquire data in the m / z range of 25-1000Da, and the cumulative time for product ion scanning was set to 0.05 s / spectrum. Product ion scanning used information-dependent acquisition (IDA) with high sensitivity mode selected. The parameters were set as follows: collision energy (CE) was fixed at 35V, segregation potential (DP) was 60V (positive polarity) and -60V (negative polarity), isotopes in the 4Da range were excluded, and the number of candidate ions monitored per cycle was 10.

[0048] (8) Information Processing: Raw mass spectrometry data were converted to MzXML files using ProteoWizard MSC onvert before being imported into the free XCMS software. The following parameters were used for peak identification: centWave m / z = 10 ppm, peak width = c(10,60), pre-filter = c(10,100). For peak grouping, bw = 5, mzwid = 0.025, and minfrac = 0.5 were used. CAMERA (a set of algorithms for metabolite profiling) was used for isotope and adduct annotation. Among the extracted ion features, only variables with more than 50% non-zero measurements in at least one group were retained. Metabolite compound identification was performed by comparing accuracy m / z values ​​(<10 ppm) and MS / MS spectra with an established internal database containing real standards.

[0049] (9) Statistical Analysis: After data normalization, data analysis was performed using the R package (ropls), including Pareto-scaled principal component analysis (PCA). The robustness of the model was assessed using 7-fold cross-validation and response permutation tests. Projected importance (VIP) values ​​were calculated for each variable to indicate its contribution to the classification. Student's t-test was applied to determine the significance of differences between the two groups in the independent samples. Metabolites with significant changes were screened using VIP > 1 and p-value < 0.05. Pearson correlation analysis was performed to determine the correlation between the two variables.

[0050] Experimental results

[0051] (1) Quality control results: Principal component analysis (PCA) results show that QC samples are tightly aggregated under both positive and negative ion modes, indicating good reproducibility of the experiment. Figure 1 ).

[0052] (2) The classification results of all identified metabolites showed that carboxylic acids and their derivatives accounted for the largest proportion, followed by fatty acyl groups, and then glycerol phospholipids. Figure 2 ).

[0053] (3) Non-targeted metabolomics detection Figure 3 Compared with the remission group after intensive treatment, the non-remission group showed 534 significantly upregulated metabolites and 365 significantly downregulated metabolites (in positive ion mode); compared with the remission group after intensive treatment, the non-remission group showed 352 significantly upregulated metabolites and 543 significantly downregulated metabolites (in negative ion mode). All differential expression results are shown in the heatmap. Figure 4 ).

[0054] (4) The radar chart shows the differences in serum metabolites and the fold change between the remission group and the non-remission group after intensive treatment. Figure 5 ).

[0055] (5) Compared with the remission group, the expression levels of four lipid metabolites in the serum of subjects in the non-remission group are shown in Table 1. The bar graphs show the fold differences. Figure 6 ).

[0056] Table 1

[0057]

[0058] (6) The ROC analysis results for the remission group and the non-remission group are as follows: Figures 7-11 As shown.

[0059] (7) Table 2 shows the efficacy of 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine in predicting the efficacy of intensive insulin therapy.

[0060] Table 2

[0061]

[0062]

[0063] ROC curve analysis showed that the AUC of 3-hydroxybutyrylcarnitine was 0.867, with a cutoff value of 6284.83, a sensitivity of 90.6%, a specificity of 83.3%, and a Youden index of 0.739; the AUC of 3-hydroxyhexadecylcarnitine was 0.734, with a cutoff value of 5199.16, a sensitivity of 93.8%, a specificity of 54.2%, and a Youden index of 0.480; the AUC of 3-methylglutarylcarnitine was 0.671, with a cutoff value of 24476.62, a sensitivity of 31.3%, a specificity of 95.8%, and a Youden index of 0.271; and the AUC of 3-methylhistidine was 0.605, with a cutoff value of 51531.08, a sensitivity of 28.1%, a specificity of 95.8%, and a Youden index of 0.239. The AUC of the four metabolite combinations was 0.88, with a sensitivity of 87.5%, a specificity of 79.2%, and a Youden index of 0.667. This indicates that 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine, and 3-methylhistidine can specifically predict the efficacy of intensive insulin therapy, especially the four metabolite combinations.

[0064] Example 2

[0065] In this section, the role of the human hepatocyte cell line HepG2 in a hyperinsulinemia-hyperglucose-induced insulin resistance model was investigated. The specific steps are as follows:

[0066] (1) Cell culture: HepG2 cells were cultured in vitro and seeded in DMEM medium containing 15% fetal bovine serum and 0.5% penicillin antibiotics. The cells were passaged when they reached 95% confluence.

[0067] (2) CCK-8 assay for cell viability: Cells were sputtered at 1×10⁻⁶ cells per cell line. 3 The cells were seeded into 96-well plates. After 12 hours, the solution was replaced with a mixture of four metabolites at concentrations of 0, 50, 100, 200, 400, and 800 μmol / L (mixing ratio 1:1:1:1), with 6 replicates per group. After 24 hours of incubation, each well was replaced with CCK-8 solution prepared with complete culture medium, and incubated for another 30 minutes. The absorbance of each well was then measured at 450 nm using a microplate reader.

[0068] (3) Detection of glucose content in culture medium supernatant: HepG2 cells were cultured at 5 × 10⁵ cells per well. 4 Cells were seeded at a density of 100 cells / well in 24-well plates. After 12 hours, the cells were further cultured in complete medium, complete medium containing a solution of four metabolites, and 10... - 7 M insulin was cultured in a high-glucose medium for 24 hours, and the supernatant was collected. The glucose content was then determined using a glucose assay kit.

[0069] (4) Western blot determination of IRS-1 protein expression in cells: Cells were washed with PBS, centrifuged to collect the cells, and pre-chilled lysis buffer was added. The cells were then incubated on ice for 10-15 minutes. Centrifuged at 12,000g for 5 minutes at 4°C. A small amount of the lysis buffer was used for protein quantification analysis. A 50 μg protein sample was subjected to sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), and then transferred to a polyvinylidene fluoride (PVDF) membrane. The membrane was blocked with TBST buffer containing 5% skim milk powder at room temperature for 2 h. Anti-IRS-1 antibody (1:1000 dilution) was added and incubated. The membrane was washed with TBST, and then HRP-labeled goat anti-rabbit IgG (1:1000 dilution) was added as a secondary antibody. The membrane was shaken at room temperature for 1 h. The PVDF membrane was then removed, washed with TBST, exposed and developed, and the target bands were scanned and analyzed. The grayscale of the bands was measured using ImageJ.

[0070] (5) Statistical analysis: SPSS 13.0 statistical software was used for one-way ANOVA. Experimental data are expressed as x±s. The t test was used for comparison between groups. P<0.05 was considered statistically significant.

[0071] Experimental results

[0072] (1) As Figure 12 As shown, when the concentrations of the four metabolites were below 400 μmol / L, the metabolite combination had no significant toxicity to HepG2 cells (P > 0.05). Since the insulin resistance model is mainly a non-invasive model, the concentration of the metabolite combination in subsequent experiments was set at 400 μmol / L.

[0073] (2) Figure 13 As shown, with the addition of the four metabolite solutions to the culture medium, the residual glucose content in the cell supernatant after induction increased compared to the control group. A similar trend was observed in the cell supernatant after induction with insulin and high-glucose medium, but the residual glucose content in the cell supernatant after insulin-high-glucose induction was the highest compared to the addition of the four metabolites. This indicates that culturing hepatocytes with the four metabolites in vitro induces insulin resistance, but not as strongly as the classic insulin model establishment method.

[0074] (3) Figure 14 As shown, compared with the control group, the expression level of IRS-1 protein decreased in the four metabolite combination intervention groups, and the expression level was the lowest in the insulin plus high glucose intervention group.

[0075] In summary, the serum levels of 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine, and 3-methylhistidine differed significantly between the remission and non-remission groups in newly diagnosed diabetic patients after intensive insulin therapy. The kit of this invention can predict the efficacy of intensive insulin therapy by detecting these serum levels. High levels of these combined components indicate a lower probability of achieving remission after intensive insulin therapy; conversely, low levels indicate a higher probability of achieving remission after intensive insulin therapy. Using the kit of this invention can predict the efficacy of intensive insulin therapy, addressing the problem of predicting differences in the efficacy of intensive insulin therapy for diabetes, and shows promising clinical application prospects.

[0076] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. The application of reagents for detecting biomarkers in the preparation of reagents for predicting the efficacy of intensive diabetes treatment, characterized in that: The biomarker is a composition consisting of 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine.

2. The application of the reagent for detecting biomarkers according to claim 1 in the preparation of reagents for predicting the efficacy of intensive diabetes treatment, characterized in that: The aforementioned intensive diabetes treatment refers to two weeks of intensive insulin therapy for newly diagnosed diabetic patients.

3. The application of the reagent for detecting biomarkers according to claim 1 in the preparation of reagents for predicting the efficacy of intensive diabetes treatment, characterized in that: The reagent used to predict the efficacy of intensive diabetes treatment is a kit that serves as an internal standard for detecting 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine, and 3-methylhistidine.

4. The application of the reagent for detecting biomarkers according to claim 3 in the preparation of reagents for predicting the efficacy of intensive diabetes treatment, characterized in that: The method of using the kit includes the following steps: placing the sample to be tested in EDTA containing the chelating agent, centrifuging, and then mixing 100 μL with 400 μL of protein removal agent; after centrifugation, drying the supernatant in a vacuum centrifuge; redissolving in acetonitrile solution with water as the solvent; and performing liquid chromatography-mass spectrometry (LC-MS) to detect the contents of 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine, and 3-methylhistidine.

5. The application of the reagent for detecting biomarkers according to claim 4 in the preparation of reagents for predicting the efficacy of intensive diabetes treatment, characterized in that: The protein removal agent is cold methanol and acetonitrile.

6. The application of the reagent for detecting biomarkers according to claim 5 in the preparation of reagents for predicting the efficacy of intensive diabetes treatment, characterized in that: The mixing ratio of the cold methanol and acetonitrile solution is 1:

1.

7. The application of the reagent for detecting biomarkers according to claim 4 in the preparation of reagents for predicting the efficacy of intensive diabetes treatment, characterized in that: The sample to be tested was serum.

8. The application of the reagent for detecting biomarkers according to claim 4 in the preparation of reagents for predicting the efficacy of intensive diabetes treatment, characterized in that: The acetonitrile solution with water as the solvent has a water-to-acetonitrile ratio of 1:1 and a volume of 100 μl.

9. The application of the reagent for detecting biomarkers according to claim 4 in the preparation of reagents for predicting the efficacy of intensive diabetes treatment, characterized in that: For LC-MS detection, hydrophilic exchange chromatography separation was performed using a 2.1 mm × 100 mm ACQUIY UPLC. Analysis was performed using a BEH 1.7 μm column in ESI positive and negative ion mode. Mobile phase A was an aqueous solution containing 25 mM ammonium acetate and 25 mM ammonium hydroxide, and mobile phase B was acetonitrile. The gradient program was as follows: initially, B was 85%, then linearly decreased to 65% after 1 minute, then decreased to 40% in 0.1 minutes, held for 4 minutes, then increased to 85% in 0.1 minutes, and reequilibrated for 5 minutes.

10. The application of the reagent for detecting biomarkers according to claim 9 in the preparation of reagents for predicting the efficacy of intensive diabetes treatment, characterized in that: The method also includes: For RPLC separation, an ACQUIY UPLCHSS T3 1.8μm column with dimensions of 2.1 mm × 100 mm was used. In ESI positive ion mode, mobile phase A was water containing 0.1% formic acid, and mobile phase B was acetonitrile containing 0.1% formic acid. In ESI negative ion mode, mobile phase A was water containing 0.5 mm ammonium fluoride, and mobile phase B was acetonitrile. The gradient program was as follows: initially 1% solution B was linearly increased to 99% over 1.5 min and held for 11.5 min, then decreased to 1% over 0.1 min, followed by a 3.4 min reequilibrium period. The gradient flow rate was 0.3 mL / min, the column temperature was kept constant at 25 °C, and 2 μL of each sample was injected. The ESI source conditions are set as follows: ion source gas 1 is set to 60, ion source gas 2 is set to 60, curtain gas is set to 30, source temperature is 600℃, and IonSpray voltage fluctuation is set to ±5500V. In single-mode mass spectrometry, the instrument is set to acquire data in the m / z range of 60-1000Da, and the cumulative time for TOF mass spectrometry is set to 0.20s / spectrum. In automatic MS / MS acquisition mode, the instrument is set to acquire data in the m / z range of 25-1000Da, and the cumulative time for product ion scanning is set to 0.05s / spectrum. Product ion scanning uses information-dependent acquisition, selects high-sensitivity mode, and sets the following parameters: collision energy is fixed at 35V, segregation potential is 60V and -60V, isotopes in the 4Da range are excluded, and the number of candidate ions monitored per cycle is 10. Information processing: Before importing the raw mass spectrometry data into the XCMS software, the raw mass spectrometry data was converted to MzXML files using Proteo Wizard MSC onvert. During peak identification, the following parameters were used: centWave m / z = 10ppm, peak width = c(10,60), pre-filter = c(10,100). For peak grouping, bw = 5, mzwid = 0.025, and minfrac = 0.5 were used. The CAMERA algorithm set for metabolite profiling annotation is used for isotope and adduct annotation; among the extracted ionic features, only variables with more than 50% non-zero measurements in at least one group are retained; compound identification of metabolites is performed by comparing the accuracy m / z value <10ppm and MS / MS spectra with an established internal database containing real standards. Statistical analysis: After data normalization, data analysis was performed using R packages, including Pareto-scaled principal component analysis (PCA). The robustness of the model was assessed through 7-fold cross-validation and response permutation tests. The projected importance (VIP) value of each variable was calculated to indicate its contribution to the classification. Student's t-test was applied to determine the significance of differences between two groups in the independent samples. Metabolites with significant changes were screened using VIP > 1 and p-value < 0.

05. Pearson correlation analysis was performed to determine the correlation between two variables.

Images