Application of biomarker in prediction of diabetes fortified treatment effect and kit

Abstract

The invention discloses an application of a biomarker in predicting the effect of diabetes fortified treatment, a kit for predicting the effect of diabetes fortified treatment and a use method of the kit, which can effectively predict the effect of diabetes fortified treatment, have high-efficiency specificity for predicting the effect of insulin fortified treatment of a patient with initial diabetes, and can be used for predicting the effect of insulin fortified treatment of the patient with initial diabetes. The problem of insulin fortified treatment effect difference prediction is solved. According to the application of the biomarker in predicting the fortified treatment effect of diabetes, the biomarker comprises 3-hydroxybutyryl carnitine, 3-hydroxyhexadecyl carnitine, 3-methylglutaryl carnitine and 3-methylhistidine.

Description

Technical Field

[0001] The present invention relates to the technical field of biomedical detection, and in particular to an application of a biomarker in predicting the effect of intensive treatment of diabetes, a kit for predicting the effect of intensive treatment of diabetes, and a method for using the kit. Background Art

[0002] Diabetes is a chronic disease that is prevalent worldwide. Diabetes is an important risk factor for cardiovascular and cerebrovascular diseases. Therefore, early identification and standardized diagnosis and treatment of diabetes are of great significance to reducing disease mortality and improving prognosis. Short-term intensive insulin therapy for newly diagnosed diabetic patients is an important means of diabetes treatment. It can enable a considerable number of patients to control blood sugar within an ideal range through diet control and proper exercise alone, but individual differences among patients can still affect prognosis. The current prediction method does not distinguish between patients' treatment drugs and dosage forms. There is a lack of a method for predicting the efficacy of intensive insulin therapy, an important treatment method. It is urgent to find a method that uses specific biomarkers to reflect the efficacy of intensive treatment in patients.

[0003] Fatty acid metabolism disorder refers to abnormalities in the production, decomposition, transportation and utilization of fatty acids in the body. These abnormalities can lead to the accumulation of fatty acids in the body, causing a variety of metabolic problems. Excessive intake or abnormal metabolism of fatty acids can lead to impaired insulin signaling, resulting in reduced glucose uptake and utilization, promoting the development of hyperglycemia, and causing insulin resistance. Relief of insulin resistance is an important prerequisite for the effectiveness of intensive insulin therapy. The current diabetes efficacy prediction methods focus on studying a variety of treatment methods for all diabetic patients. The predictive effect of the early stage of diabetes treatment, which is a key stage of treatment, and intensive treatment, an important means, is still unknown. Therefore, the metabolomics detection of serum of newly diagnosed diabetic patients by liquid chromatography-mass spectrometry (LC-MS) can screen and analyze the application of biomarkers with good sensitivity and specificity in predicting the effect of intensive treatment of diabetes, which can fill the gap in predictive indicators for the effect of intensive treatment of newly diagnosed diabetic patients. Summary of the invention

[0004] In order to overcome the defects 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 of diabetes, which can effectively predict the effect of intensive treatment of diabetes, has high efficiency and specificity in predicting the efficacy of intensive insulin treatment in patients with newly diagnosed diabetes, and solves the problem of predicting differences in the efficacy of intensive insulin treatment.

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

[0006] The beneficial effects of the present invention are: being able to effectively predict the effect of intensive treatment of diabetes, having high efficiency and specificity in predicting the efficacy of intensive insulin treatment for newly diagnosed diabetic patients, and solving the problem of predicting the difference in efficacy of intensive insulin treatment.

[0007] Also provided is a kit for predicting the effect of intensive treatment of diabetes, which comprises internal standards for detecting 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine.

[0008] Also provided is a method for using a kit for predicting the effect of intensive treatment of diabetes, which comprises the following steps: placing a sample to be tested in a solution containing a chelating agent EDTA, taking 100 μl of the sample and mixing it with 400 μl of a protein remover after centrifugation; drying the supernatant in a vacuum centrifuge after centrifugation; redissolving the sample in an acetonitrile solution whose solvent is water; and performing liquid chromatography-mass spectrometry (LC-MS) detection to obtain the contents of 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0011] Figure 3 A is the volcano plot of differential metabolites between the remission group and the non-remission group (positive ion mode); Figure 3 B is the volcano plot of differential metabolites between the remission group and the non-remission group (negative ion mode).

[0012] Figure 4 A is the heat map of differential metabolites between the remission group and the non-remission group (positive ion mode); Figure 4 B is the heat map of differential metabolites between the remission group and the non-remission group (negative ion mode).

[0013] Figure 5 A is the radar chart of the difference fold of differential metabolites between the remission group and the non-remission group; Figure 5 B is the radar chart of the difference fold of differential metabolites between the remission group and the non-remission group.

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

[0015] Figure 7 This is the ROC analysis result of 3-hydroxybutyrylcarnitine in the remission group and the non-remission group.

[0016] Figure 8 This is the ROC analysis result of 3-methylhistidine in the remission group and the non-remission group.

[0017] Fig. 9 This is the ROC analysis result of 3-hydroxyhexadecylcarnitine in the remission group and the non-remission group.

[0018] Fig.10 This is the ROC analysis result of 3-methylglutarylcarnitine in the remission group and the non-remission group.

[0019] Fig.11 This is the ROC analysis result of the remission group and the non-remission group.

[0020] Fig.12 HepG2 cell viability assay.

[0021] Fig.13 Detection of glucose content in the supernatant of HepG2 cells.

[0022] Fig.14 This is the Western blot band and quantitative analysis bar graph of HepG2 cells. DETAILED DESCRIPTION

[0023] The invention discloses an application of the biomarker in predicting the effect of intensive treatment of diabetes, wherein the biomarker comprises 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine.

[0024] The beneficial effects of the present invention are: being able to effectively predict the effect of intensive treatment of diabetes, having high efficiency and specificity in predicting the efficacy of intensive insulin treatment for newly diagnosed diabetic patients, and solving the problem of predicting the difference in efficacy of intensive insulin treatment.

[0025] Preferably, the intensive treatment of diabetes is 2 weeks of intensive insulin treatment for newly diagnosed diabetic patients.

[0026] Also provided is a kit for predicting the effect of intensive treatment of diabetes, which comprises internal standards for detecting 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine.

[0027] Also provided is a method for using a kit for predicting the effect of intensive treatment of diabetes, which comprises the following steps: placing a sample to be tested in a solution containing a chelating agent EDTA, taking 100 μl of the sample and mixing it with 400 μl of a protein remover after centrifugation; drying the supernatant in a vacuum centrifuge after centrifugation; redissolving the sample in an acetonitrile solution whose solvent is water; and performing liquid chromatography-mass spectrometry (LC-MS) detection to obtain 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 solutions is 1:1.

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

[0031] Preferably, the solvent is water-acetonitrile solution, the mixing ratio of water to acetonitrile is 1:1, and the volume is 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 modes, the mobile phase A is an aqueous solution containing 25 mM ammonium acetate and 25 mM ammonium hydroxide, and the mobile phase B is acetonitrile; the gradient program is: B is 85% initially, linearly reduced to 65% after 1 minute, then reduced to 40% within 0.1 minute, maintained for 4 minutes, then increased to 85% within 0.1 minute, and re-equilibrated for 5 minutes.

[0033] Preferably, the method further comprises:

[0034] For reversed phase liquid chromatography RPLC separation, an ACQUIYUPLC HSS T3 1.8 μm 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 an initial 1% B solution linearly increased to 99% within 1.5 minutes and maintained for 11.5 minutes, then reduced to 1% within 0.1 minutes, followed by a 3.4-minute re-equilibrium 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 were set as follows: ion source gas 1 was set to 60, ion source gas 2 was set to 60, curtain gas was set to 30, source temperature was 600°C, and IonSpray voltage floating was set to ±5500V; in the single mass spectrometry scanning mode, the instrument was set to collect in the m / z range of 60-1000Da, and the accumulation time of TOF mass spectrometry scanning was set to 0.20s / spectrum; in the automatic MS / MS acquisition mode, the instrument was set to collect in the m / z range of 25-1000Da, and the accumulation time of product ion scanning was set to 0.05s / spectrum; product ion scanning adopted information-dependent acquisition, and the high sensitivity mode was selected, and the parameters were set as follows: the collision energy was fixed at 35V, the partial potential was 60V and -60V, isotopes within the 4Da range were excluded, and the number of candidate ions monitored in each cycle was 10;

[0036] Information processing: The 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), prefilter = c(10,100); for peak grouping, bw = 5, mzwid = 0.025, minfrac = 0.5 were used; the algorithm set CAMERA for metabolite profile annotation 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, and compound identification of metabolites was performed by comparing accuracy m / z values ​​< 10 ppm and MS / MS spectra with an established in-house database containing authentic standards;

[0037] Statistical analysis: After data were normalized and processed, R packages were used for data analysis, including principal component analysis (PCA) with Pareto scaling. The robustness of the model was assessed by 7-fold cross validation and response permutation test. The projection importance (VIP) value of each variable was calculated to indicate its contribution to the classification. The Student's t-test was applied to determine the significance of the difference between two groups of independent samples. VIP>1 and p-value <0.05 were used to screen significantly changed metabolites. Pearson correlation analysis was performed to determine the correlation between two variables.

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

[0039] Embodiment 1

[0040] A kit for predicting the effect of intensive treatment on newly diagnosed diabetic patients, comprising: internal standard substances, methanol, acetonitrile and pure water required for detecting 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine.

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

[0042] (1) According to the inclusion criteria: the subjects were diagnosed with diabetes according to the WHO 1999 diagnostic criteria for diabetes, with a disease duration of 0-5 years. Exclusion criteria: type 1 diabetes, ketoacidosis, infection and other stressful conditions, excluding other autoimmune diseases, liver and kidney diseases. The subjects were given baseline general information and blood glucose, blood lipids, insulin, glycosylated hemoglobin (HbA1c) and other tests, and then given insulin pump or multiple subcutaneous insulin injections for intensive treatment. Fasting blood glucose (FPG) <6.5mmol / L and 2-hour blood glucose (2hPG) <8.0mmol / L were considered treatment targets. After reaching the target, treatment was maintained for 2 weeks, and baseline examinations were repeated 24 hours after insulin was discontinued. After discharge, diet control, appropriate exercise, and blood glucose monitoring were continued. HbA1c and blood glucose were rechecked 3 months after stopping treatment. FPG <7.0mmol / L and 2hPG <10mmol / L were considered remission, otherwise they were considered not remission.

[0043] (2) Take 5 mL of EDTA-containing vacuum tubes and draw 3 mL of cubital venous blood from the subjects in an 8-hour fasting state, centrifuge for 15 min (1500 g, 4°C). Each aliquot (150 μL) of plasma samples was stored at -80°C until UPLC-Q-TOF / MS analysis.

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

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

[0046] (5) LC-MS analysis: Detection was performed at Shanghai Applied Protein Technology Co., Ltd. using an ultra-high performance liquid chromatograph (1290 Infinity LC, Agilent Technologies) coupled to a quadrupole time-of-flight mass spectrometer (AB Sciex TripleTOF 6600). For hydrophilic exchange chromatography, samples were analyzed using a 2.1 mm × 100 mm ACQUIY UPLC BEH 1.7 μm column (Waters, Ireland). In the 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: B was 85% at the beginning, linearly decreased to 65% (over 11 minutes) after 1 minute, then decreased to 40% within 0.1 minutes, maintained for 4 minutes, and then increased to 85% within 0.1 minutes, and re-equilibrated for 5 minutes.

[0047] (6) For reverse 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 the 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 the 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 an initial 1% B solution linearly increased to 99% within 1.5 min and maintained for 11.5 min, then decreased to 1% within 0.1 min, followed by a re-equilibration period of 3.4 min. 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°C, and IonSpray voltage floating (ISVF) was set to ±5500 V. In the mass spectrometry (MS) single mode, the instrument was set to collect in the m / z range of 60-1000 Da, and the accumulation time of TOF mass spectrometry scan was set to 0.20 s / spectrum. In the automatic MS / MS acquisition mode, the instrument was set to collect in the m / z range of 25-1000 Da, and the accumulation time of product ion scan was set to 0.05 s / spectrum. Information dependent acquisition (IDA) was used for product ion scan, and high sensitivity mode was selected. The parameters were set as follows: collision energy (CE) was fixed at 35 V, partial polarity (DP) was 60 V (positive polarity) and -60 V (negative polarity), isotopes within the 4 Da range were excluded, and the number of candidate ions monitored per cycle was 10.

[0048] (8) Information processing: The raw mass spectrometry data were converted to MzXML files using ProteoWizard MSC convert before being imported into the freely available XCMS software. During peak identification, the following parameters were used: centWave m / z = 10 ppm, peak width = c(10,60), prefilter = c(10,100). For peak grouping, bw = 5, mzwid = 0.025, minfrac = 0.5 were used. CAMERA (a collection of algorithms for metabolite profile annotation) was used for annotation of isotopes and adducts. Among the extracted ion features, only variables with more than 50% non-zero measurements in at least one group were retained. Compound identification of metabolites was performed by comparing the accuracy m / z values ​​(<10 ppm) and MS / MS spectra with an established in-house database containing authentic standards.

[0049] (9) Statistical analysis: After data were normalized, the R package (ropls) was used for data analysis, including principal component analysis (PCA) with Pareto scaling. The robustness of the model was evaluated by 7-fold cross validation and response permutation test. The projection importance (VIP) value of each variable was calculated to indicate its contribution to the classification. The Student's t test was applied to determine the significance of the difference between two groups of independent samples. VIP>1 and p value <0.05 were used to screen significantly changed metabolites. Pearson correlation analysis was performed to determine the correlation between two variables.

[0050] Experimental Results

[0051] (1) Quality control results: The principal component analysis (PCA) evaluation results of the samples showed that the QC samples in the positive and negative ion modes were closely clustered together, indicating that the experimental repeatability was good ( 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 testing Figure 3 ) found that compared with the remission group after intensive treatment, 534 significantly up-regulated metabolites and 365 significantly down-regulated metabolites were detected in the non-remission group (in positive ion mode); compared with the remission group after intensive treatment, 352 significantly up-regulated metabolites and 543 significantly down-regulated metabolites were detected in the non-remission group (in negative ion mode). All differential expression results are displayed in the heat map ( Figure 4 ).

[0054] (4) The radar chart shows the differences and multiples of serum metabolites 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 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine in the serum of the subjects in the non-remission group are shown in Table 1. The bar graph shows the difference fold ( Figure 6 ).

[0056] Table 1

[0057]

[0058] (6) The ROC analysis results of the remission group and the non-remission group are as follows Figure 7-Figure 11 shown.

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

[0060] Table 2

[0061]

[0062]

[0063] ROC curve analysis showed that the AUC value of 3-hydroxybutyrylcarnitine was 0.867, when the cutoff value was 6284.83, the sensitivity was 90.6%, the specificity was 83.3%, and the Youden index was 0.739; the AUC value of 3-hydroxyhexadecylcarnitine was 0.734, the cutoff value was 5199.16, the sensitivity was 93.8%, the specificity was 54.2%, and the Youden index was 0.480; the AUC value of 3-methylglutarylcarnitine was 0.671, the cutoff value was 24476.62, the sensitivity was 31.3%, the specificity was 95.8%, and the Youden index was 0.271; the AUC value of 3-methylhistidine was 0.605, the cutoff value was 51531.08, the sensitivity was 28.1%, the specificity was 95.8%, and the Youden index was 0.239. The AUC value of the combination of the four metabolites was 0.88, the sensitivity was 87.5%, the specificity was 79.2%, and the Youden index was 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 combination of the four metabolites.

[0064] Embodiment 2

[0065] In this part, the human hepatocyte cell line HepG2 was used to study the effects of high insulin plus high glucose induced insulin resistance model. The specific steps are as follows:

[0066] (1) Cell culture: HepG2 cells were cultured in vitro and inoculated in DMEM medium containing 15% fetal bovine serum and 0.5% double antibody. Cells were passaged when the confluence reached 95%.

[0067] (2) CCK-8 method to detect cell viability: 1×10 3 / well was inoculated in a 96-well plate. After 12 hours, the four metabolite mixed solutions of 0, 50, 100, 200, 400, and 800 μmol / L were replaced (mixing ratio 1:1:1:1), with 6 replicates in each group. After 24 hours of culture, each well was replaced with CCK-8 solution prepared with complete culture medium, and incubated in the incubator for another 30 minutes. The absorbance of each well was detected at 450 nm using an enzyme reader.

[0068] (3) Glucose content in culture medium supernatant: HepG2 cells were cultured at 5×10 4 Cells were seeded in 24-well plates at a density of 10 cells / mL. After 12 hours, the cells were further incubated in complete medium, complete medium containing the four metabolite solutions, 10 - 7 The cells were cultured in high-glucose medium with 1 M insulin for 24 h, and the supernatants were collected to measure the glucose content using a glucose detection kit.

[0069] (4) Western blot analysis of IRS-1 protein expression in cells: After washing with PBS, the cells were collected by centrifugation, and pre-cooled lysis buffer was added. The cells were placed on ice for 10-15 minutes. Centrifuged at 12,000 g for 5 minutes at 4°C. A small amount of lysate was taken for protein quantitative analysis. A 50 μg protein sample was taken for sodium dodecylsulfate polyacrylamide gel electrophoresis (SDS-PAGE), and then transferred to a polyvinylidenefluoride (PVDF) membrane, blocked with 5% skim milk powder in TBST buffer (tris buffered saline with tween-20, TBST) at room temperature for 2 h, incubated with anti-IRS-1 (1:1000 dilution) antibody, rinsed with TBST, and then added with 1:1000 dilution HRP-labeled goat anti-rabbit IgG as secondary antibody. The reaction was shaken at room temperature for 1 h, the PVDF membrane was removed, rinsed with TBST, exposed and developed, the target bands were scanned and analyzed, and the grayscale of the bands was determined using Image J.

[0070] (5) Statistical analysis: SPSS13.0 statistical software was used for one-way analysis of variance. The experimental data were 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) Fig.12 As shown in the data, when the concentrations of the four metabolites were within 400 μmol / L, the metabolite combination had no significant toxicity to HepG2 cells (P>0.05), and because the insulin resistance model was mainly a non-damage model, the metabolite combination concentration in subsequent experiments was set at 400 μmol / L.

[0073] (2) Fig.13 As shown in the figure, with the addition of the four metabolite solutions to the culture medium, the remaining glucose content in the induced cell supernatant increased compared with the control group. The same trend also appeared in the cell supernatant after the cells were induced by the culture medium with insulin plus high glucose. However, compared with the addition of the four metabolites, the remaining glucose content in the cell supernatant after insulin plus high glucose induction was the highest. This shows that the four metabolites cultured hepatocytes in vitro will cause insulin resistance, but not as good as the classic insulin model establishment method.

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

[0075] In summary, the levels of 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine in the serum of newly diagnosed diabetic patients who were divided into the remission group and the non-remission group after insulin intensive treatment were significantly different. The kit of the present invention can predict the effect of insulin intensive treatment by detecting the levels of 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine in serum. If the level of the combination of 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine is high, the probability of achieving remission after insulin intensive treatment is low; if the level of the combination of 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine is low, the probability of the patient achieving remission after insulin intensive treatment is high. The use of the kit of the present invention can achieve the purpose of predicting the effect of insulin intensive treatment, and is used to predict the effect of diabetes intensive treatment to solve the problem of predicting the difference in the efficacy of insulin intensive treatment, and has a good prospect for clinical application.

[0076] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any simple modification, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. Application of biomarkers in predicting the effect of intensive treatment of diabetes, characterized by: The biomarkers include: 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine.

2. The use of the biomarker according to claim 1 in predicting the effect of intensive treatment of diabetes mellitus, characterized in that: The diabetes intensive treatment is 2 weeks of insulin intensive treatment for newly diagnosed diabetes patients.

3. A kit for predicting the effect of intensive treatment of diabetes, characterized in that: It includes internal standards for the detection of 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine, and 3-methylhistidine.

4. The method for using the kit for predicting the effect of intensive treatment of diabetes according to claim 3, characterized in that: It includes the following steps: The sample to be tested was placed in EDTA containing chelating agent, and after centrifugation, 100 μl was taken and mixed with 400 μl protein remover; After centrifugation, the supernatant is dried in a vacuum centrifuge; it is redissolved in an acetonitrile solution whose solvent is water; and liquid chromatography-mass spectrometry LC-MS detection is performed to obtain the contents of 3-hydroxybutyrylcarnitine, 3-hydroxyhexadecylcarnitine, 3-methylglutarylcarnitine and 3-methylhistidine.

5. The method for using the kit for predicting the effect of intensive treatment of diabetes according to claim 4, characterized in that: The protein removal agents are cold methanol and acetonitrile.

6. The method for using the kit for predicting the effect of intensive treatment of diabetes according to claim 5, characterized in that: The mixing ratio of the cold methanol and acetonitrile solutions is 1:

1.

7. The method for using the kit for predicting the effect of intensive treatment of diabetes according to claim 4, characterized in that: The sample to be tested is serum.

8. The method for using the kit for predicting the effect of intensive treatment of diabetes according to claim 4, characterized in that: The solvent is water-acetonitrile solution, in which the mixing ratio of water to acetonitrile is 1:1 and the volume is 100 μl.

9. The method for using the kit for predicting the effect of intensive treatment of diabetes according to claim 4, characterized in that: In LC-MS detection, for hydrophilic exchange chromatography separation, the samples were analyzed using a 2.1 mm × 100 mm ACQUIY UPLC BEH1.7 μm column in ESI positive and negative ion modes with mobile phase A containing 25 mM ammonium acetate and 25 mM The mobile phase B was acetonitrile; the gradient program was: B was 85% at the beginning, linearly decreased to 65% after 1 minute, then decreased to 40% in 0.1 minute, and maintained for 4 minute, followed by an increase to 85% over 0.1 minute and a 5 minute re-equilibration.

10. The method for using the kit for predicting the effect of intensive treatment of diabetes according to claim 9, characterized in that: The method further includes: For reverse phase liquid chromatography (RPLC) separation, a 2.1 mm × 100 ACQUIY UPLC HSS T3 1.8 μm column with 100 μm diameter; 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 an initial 1% B solution linearly increased to 99% within 1.5 minutes and maintained for 11.5 minutes, then decreased to 1% within 0.1 minutes, followed by a 3.4-minute re-equilibrium 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 were set as follows: ion source gas 1 was set to 60, ion source gas 2 was set to 60, curtain gas was set to 30, source temperature was 600°C, and IonSpray voltage floating was set to ±5500 V; in the mass spectrometry scanning single mode, the instrument was set to collect in the m / z range of 60-1000 Da, and the accumulation time of the TOF mass spectrometry scanning was set to 0.20 s / spectrum; in the automatic MS / MS In acquisition mode, the instrument was set to collect in the m / z range of 25-1000Da, and the accumulation time of product ion scanning was set to 0.05s / spectrum; product ion scanning adopted information-dependent acquisition, and the high sensitivity mode was selected, and the parameters were set as follows: the collision energy was fixed at 35V, the bias potential was 60V and -60V, isotopes within the range of 4Da were excluded, and the number of candidate ions monitored in each cycle was 10; Information processing: The raw mass spectrometry data were converted into MzXML files using Proteo Wizard MSC onvert before being imported into XCMS software; during peak identification, the following parameters were used: centWave m / z = 10 ppm, peak width = c(10,60), prefilter = c(10,100); for peak grouping, bw = 5, mzwid = 0.025, minfrac = 0.5 were used; the algorithm set CAMERA for metabolite profile annotation was used for the annotation of isotopes and adducts; among the extracted ion features, only variables with more than 50% non-zero measurements in at least one group were retained, and compound identification of metabolites was performed by comparing the accuracy m / z value < 10 ppm and the MS / MS spectra with the established in-house database containing authentic standards; Statistical analysis: After data were normalized and processed, R packages were used for data analysis, including principal component analysis (PCA) with Pareto scaling. The robustness of the model was assessed by 7-fold cross validation and response permutation test. The projection importance (VIP) value of each variable was calculated to indicate its contribution to the classification. The Student's t-test was applied to determine the significance of the difference between two groups of independent samples. VIP>1 and p-value <0.05 were used to screen significantly changed metabolites. Pearson correlation analysis was performed to determine the correlation between two variables.

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