CN115356490B

CN115356490B - Biomarker for diagnosis of type II diabetes mellitus, kit and application thereof

Info

Publication number: CN115356490B
Application number: CN202210989596.7A
Authority: CN
Inventors: 姜长涛; 乔杰; 庞艳莉; 徐枫; 聂启兴; 汪锴
Original assignee: Peking University Third Hospital Peking University Third Clinical Medical College
Current assignee: Peking University Third Hospital Peking University Third Clinical Medical College
Priority date: 2022-08-17
Filing date: 2022-08-17
Publication date: 2023-05-05
Anticipated expiration: 2042-08-17
Also published as: CN115356490A

Abstract

The invention provides a group of biomarkers for detecting type II diabetes, and further provides a kit for detecting type II diabetes containing the biomarkers and a using method thereof, which can be used for rapid and efficient diagnosis of type II diabetes.

Description

Biomarker for diagnosis of type II diabetes mellitus, kit and application thereof

Technical Field

The invention belongs to the technical field of biological detection, and particularly relates to the field of biomarkers and kits for diagnosis and detection of type II diabetes.

Background

Diabetes mellitus (Diabetes mellitus, DM) is a type of metabolic disease induced by insufficient secretion of insulin by islet beta cells or reduced sensitivity of peripheral tissue cells to insulin. The clinical characteristics of the medicine are mainly that the medicine is continuously hyperglycemic caused by sugar metabolism disorder, and is accompanied by metabolic disorder such as protein, lipid, electrolyte and the like and imbalance of in-vivo acid-base ratio. Diabetes is largely classified into Type one diabetes (Type 1 diabetes, T1D), type two diabetes (Type 2 diabetes, T2D), gestational diabetes (Gestational diabetes mellitus, GDM), and special Type diabetes (monogenic diabetes, drug or chemical induced diabetes, etc.), wherein T2D accounts for about 90% of the total number. Diabetes mellitus and its complications have become a globally significant public health problem in the 2l century. Diabetes is the third non-infectious chronic disease which threatens human health after tumor and cardiovascular and cerebrovascular diseases in China, and not only causes serious harm to human, but also brings great burden to social and economic development.

Traditional type two diabetes diagnosis relies mainly on blood glucose testing. According to the guidelines for prevention and treatment of type 2 diabetes in China (2021 edition), one of the following two needs to be satisfied: random blood sugar is more than or equal to 11.1 mmol/L; the fasting blood sugar is more than or equal to 7 mmol/L. Comprehensive determinations are often made in connection with clinical symptom diagnosis or glycosylated hemoglobin (HbA 1C) levels.

Meanwhile, the clinical manifestations of type II diabetes have high heterogeneity, and the problems above make diagnosis and treatment of T2D have become hot spots and difficult problems which are concerned together in the field of endocrine metabolism. Therefore, a noninvasive biomarker diagnosis method easy to detect is found, and the method is combined with the existing clinical indexes to predict and diagnose the type II diabetes mellitus, so that the method has great significance for disease risk assessment of the type II diabetes mellitus. At present, no rapid and efficient diagnosis kit for the type II diabetes exists in the market, so that early detection of the type II diabetes is seriously hindered, and diagnosis and treatment of patients are delayed.

Disclosure of Invention

In order to fill the blank in the prior art, the invention provides a group of biomarkers which can be used for detecting the type II diabetes mellitus, and further provides a kit comprising the biomarkers for detecting the type II diabetes mellitus and application thereof.

The biomarker of the present invention refers to a metabolite component present in a biological sample of a subject, which may be a human or a mammal. The biological sample may be selected from the group consisting of stool, plasma, or serum derived from a subject; faeces are preferred in the present invention. In a first aspect, the invention discloses biomarkers useful in the diagnosis of type II diabetes comprising a plurality of sphingolipids metabolites selected from the group consisting of sphingolipids, said metabolite combinations comprising at least one sphingolipid differentially expressed in at least one target tissue or sample and in one control sample. The biomarkers can be used in the diagnosis of type II diabetes in a subject.

The metabolites are selected from one or more of the following metabolites: cer16:0, cer18:0, cer20:0, cer24:0, KDS18, SM18:0.

The use of said biomarker for the manufacture of a kit for the test of type two diabetes in a subject, said kit comprising, in a method of use, a test for type two diabetes in a subject by testing the level of a metabolite in the subject's stool, plasma or serum.

As one of the preferred embodiments, the metabolites include Cer18:0 and KDS18. Further provided is a diagnostic kit for type II diabetes comprising the two metabolites, a standard solution comprising Cer18:0 and KDS18 and an internal standard solution, wherein the internal standard solution refers to isotopically labeled diagnostic markers Cer18:0 and KDS18, and the isotopic labeling mode can be as follows ² H or ¹³ C. The kit also comprises an extract liquid, wherein the extract liquid consists of methanol and chloroform, and the volume ratio of the methanol to the chloroform is as follows: 1:1-5:1, and further may comprise 700 μl of 96-well plates, 350 μl of V-shaped 96-well plates, shrouded silica gel, 96-well sealed aluminum membranes.

The kit is used for diagnosing type II diabetes by measuring the level of sphingolipids in the feces, serum or plasma of a subject, and inputting the measured values into a random forest model to obtain a score cut-off value.

The application method of the kit for diagnosing the type II diabetes comprises the following steps of: a) Preparing metabolite standard substance solutions with different concentrations: preparing solutions with different concentrations by using standard solutions of Cer18:0 and KDS18 respectively, placing the solutions and a blank into a centrifuge tube together, and centrifuging for 10-30 minutes under 4000-10000 revolutions of a table centrifuge; adding 200 microliters of freshly prepared chloroform-methanol solution into each centrifuge tube, shaking vigorously, shaking and dissolving for 10-15 minutes at 800-1200 rpm, and standing for later use; b) Preparing an internal standard substance solution: 3 ml of methanol is taken as an internal standard diluent, added into an internal standard solution, covered with a cover, vigorously shaken, and left to stand for about 15 minutes for dissolution. Diluting an internal standard solution, and adding the internal standard solution into a 96-well micro-pore plate; c) Preparation of serum or plasma samples: taking out 700 microliter of the microplate provided by the kit, sequentially adding 5 microliter of standard 1 to standard 7 and blank control into the A1 to A8 wells, and adding 5 microliter of serum (or plasma) sample or 5 microliter of low, medium and high concentration quality control into other wells. To each well was added 25. Mu.l of the internal standard solution, covered with a silica gel cap and shaken at 1000rpm for 10min. Centrifuge at 2000g for 2 min. The silica gel cover is taken down lightly, so that liquid in the micro-pore plate is prevented from splashing, and the silica gel pad is properly placed to prevent pollution for standby; reacting at 1450rpm at 30 ℃ for 60 minutes, carefully taking down the silica gel pad, and properly placing the silica gel pad to prevent pollution for later use; 350 microliters of loading buffer is added to each well, and the silica gel cover is covered for vigorous shaking; standing at-20 ℃ for 20 minutes, and centrifuging 2000g for 20 minutes; taking down the silica gel cover, carefully sucking 150 microliters of supernatant into a clean V-shaped bottom micro-pore plate, covering an aluminum foil envelope, and putting into an automatic sampler; d) Measuring the sample prepared in c) by liquid chromatography and mass spectrometry, and calculating the concentration of the metabolite in the sample; e) Inputting the concentration of the biomarker calculated in the step d) into a random forest model for calculation, and judging whether the tested body has the type II diabetes according to the score.

The random forest model to which the present invention applies may be commercially available software, which may optionally also be part of the kit described above. Preferably, the software package is part of a kit.

Drawings

The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:

FIG. 1a shows the results of fecal sphingolipid index measurements for numbers H1-H11;

FIG. 1b shows the results of fecal sphingolipid index measurements for SEQ ID NO. H12-H24;

FIG. 1c is a graph showing the results of fecal sphingolipid index measurements for numbers H25-D12;

FIG. 1D shows the results of fecal sphingolipid index measurements for numbers D13-H25;

FIG. 2 diagnostic ability of all sphingolipids combined;

FIG. 3 diagnostic ability of specific sphingolipid combinations (Cer18:0 and KDS 18).

Detailed Description

The following examples are provided to further illustrate the substance of the present invention, but are not intended to limit the scope of the present invention. Although the invention has been described in detail with reference to preferred embodiments, those skilled in the art will understand that modifications and equivalents may be made to the present invention without departing from the spirit and scope of the invention. The experimental methods for which specific conditions are not specified in the examples are generally conducted under conventional conditions, such as those described in textbooks and experimental guidelines, or under conditions recommended by the manufacturer.

Example 1: screening for differential biomarkers

The test specimens in the present invention were approved by the local ethics committee and informed consent was obtained for all subjects. The invention is used for respectively detecting the content of sphingolipid in stool specimens of 25 healthy people and 25 confirmed diabetics and detecting corresponding clinical indexes by 50 subjects in the group by using an ultra-high performance liquid chromatography-tandem mass spectrometry technology. The detection method is as follows. The detection results are shown in Table 1.

1. Collection and preparation of fecal samples

Fecal samples from each subject were collected 5-10 g in sterile plastic centrifuge tubes.

The acquisition process comprises the following steps:

1) Preparing a clean bedpan, a sterile acquisition rod and a sterile plastic centrifuge tube.

2) At room temperature (about 25 degrees celsius), fresh fecal specimens were picked and placed into sterile plastic centrifuge tubes.

3) The centrifuge tube is marked with the specimen number and necessary information.

4) After quick freezing by liquid nitrogen, the mixture is quickly put into a refrigerator at-80 ℃ for storage.

2. Serum clinical marker detection

Hematology and biochemistry tests were performed using a LH750 hematology analyzer and a syncron DXC800 clinical system according to the manufacturer's protocol.

3. Sphingolipid detection in fecal samples:

sample preparation: accurate weighing of about 20. 20 mg manure was done in a centrifuge tube of 1.5 mL, and weight was recorded. 100. Mu.L of double distilled water and zirconia beads were added and homogenized well in a histiocyte mill. 10. Mu.L of methanol (containing an internal standard, 500 nM d 7-sphinganine d18:1 (sphingosine), 2.5. Mu. M d7-ceramide d18:1/15:0 (ceramide) and pre-chilled 400. Mu.L of extractant (chloroform/methanol=2:1) were added, vortexing and mixing for 10 minutes, followed by centrifugation at 12000 rpm at 4℃for 10 minutes, transferring the supernatant to a fresh 1.5 mL centrifuge tube, vacuum concentrating and volatilizing the sample, adding pre-chilled 100. Mu.L of complex solution (methanol/isopropanol=4:1), vortexing and mixing for 10 minutes, centrifuging at 18000 rpm at 4℃for 10 minutes, and collecting the supernatant for analysis by UPLC-QTRAP/MS (ultra high Performance liquid chromatography tandem ion trap mass spectrometry).

Analytical instrument testing: UPLC-QTRAP/MS: the separation system employs a waters ultra-high performance liquid chromatography system (waters, usa) equipped with a binary solvent controller, column incubator and autosampler. The detection system employed an AB SCIEX QTRAP 5500 ion trap mass spectrometer (AB SCIEX Co., USA) equipped with a Turbo V electrospray ion source.

Chromatographic conditions: the column was used with a Watertian UPLC CSH C18 column (100 mm X2.1 mm, 1.7 μm); column temperature 40 ℃; the temperature of the automatic sampler is 4 ℃; mobile phase a:30% water/70% methanol (0.4% formic acid), B:70% methanol/30% isopropanol (0.4% formic acid); the flow rate is 0.2 mL/min; the sample injection amount is 2 mu L; gradient elution procedure: 0-1.5 min (40-60% B), 1.5-6 min (60-80% B), 6-8 min (80-90% B), 8-9.5 min (90-95% B), 9.5-17 min (95-98% B), 17-19 min (98-40% B).

Mass spectrometry conditions: electrospray ion sources employ positive ion scanning (esi+), multiple reactive ion monitoring mode (MRM mode) to collect data. The specific conditions are as follows: air curtain (35L/h), medium intensity of collision air (Medium), electrospray voltage (5500V), ion source temperature (550 ℃), atomizing air and drying air (55L/h). And optimizing the declustering voltage (DP) and Collision Energy (CE) of each diagnostic marker standard to be tested by adopting a needle pump.

Determination of the concentration of diagnostic markers to be tested: drawing a standard curve according to the concentration of the standard substance solution of the diagnostic marker to be detected and the corresponding area ratio of the diagnostic marker to be detected and the stable isotope internal standard, and quantitatively measuring by adopting an isotope internal standard method. And simultaneously, according to the biological sample detection guiding principle, the quality control of the sample detection process is carried out by adding an isotope internal standard into the sample.

The stool sample test results are shown in FIGS. 1 a-1D (in the T2D index shown in the figures, "1" represents healthy volunteers and "2" represents established type II diabetes).

Example 2: specific sphingolipid combinations differentiate healthy and second diabetes groups

To distinguish healthy people from type two diabetics, we use a one-factor Wilcoxon rank sum test and LASSO to pick and identify candidate biomarkers and use a random forest model to evaluate candidate variables and build a model.

Random forest model building method and related parameter selection: the correlation model is built in the R-Studio environment of the R language. Firstly, cleaning and preprocessing original data, wherein missing data of the data set adopts a miss forest () method of a random forest iteration method to fill in missing values. And dividing the data according to the ratio of 7/3, and establishing a training set and a testing set. Further, we construct an SVM model using the e1071 package, select tuning parameters using tune.svm function in experiments, tune parameters using ten fold cross validation. Finally, selecting the Sigmoid kernel function as an optimal modeling model.

The random forest model marking method comprises the following steps:

1) Acquiring data of healthy people and type II diabetes mellitus patients, carrying out normalization processing on the acquired sample data, and further dividing the sample data after normalization processing into a test set and a training set;

2) And selecting the decision tree type as CART, respectively carrying out decision tree training on each training set to obtain corresponding CART decision tree models trained by each training set, and obtaining N-1 CART decision tree models.

3) Evaluating all features in each CART decision tree model according to the feature importance to obtain a feature set meeting a preset condition in each CART decision tree model; firstly, determining a CART decision tree model to be evaluated currently, and summarizing all features of training data in a corresponding training set in the CART decision tree model to be evaluated currently; secondly, calculating the base index score of each feature in the CART decision tree model to be evaluated currently, and after all the calculated base index scores in the CART decision tree model to be evaluated currently are arranged according to the preset feature importance, obtaining a feature set arranged according to the feature importance descending order in the CART decision tree model to be evaluated currently; and finally, repeatedly carrying out feature elimination in the feature set in the order of from small to large in feature importance according to the preset elimination proportion until the feature quantity in the feature set reaches a threshold value (such as m), and obtaining the feature set after feature elimination in the CART decision tree model to be evaluated currently. Wherein m can be set as the square of the total feature number in the CART decision tree model to be evaluated currently.

From the random forest model, we found that sphingolipids in human faeces samples have predictive capacity for type two diabetes (Table 1, FIG. 2), and especially that the combination of two sphingolipids of Cer18:0 and KDS18 can achieve 100% specificity and 87% sensitivity for type two diabetes, with ROC values as high as 0.9531 (Table 2, FIG. 3).

Table 1: diagnostic capabilities of sphingolipid combinations

Labeling: the cut-off value is the maximum value of the sum of sensitivity and specificity in the training set.

Table 2: diagnostic ability of specific sphingolipid combinations (Cer 18:0 and KDS 18)

Claims

1. Use of a reagent for detecting a biomarker in the preparation of a kit for diagnosis of type two diabetes mellitus, wherein the biomarker is a sphingolipid component comprising at least Cer18:0 and KDS18 in a fecal sample of a subject.

2. The use of claim 1, wherein the sphingolipid composition comprises, in addition to Cer18:0 and KDS18, one or more of the following sphingolipid compositions: cer14:0, cer16:0, cer18:1, cer2:0, cer20:0, cer22:0, cer24:0, cer24:1, cer16:0, DHCer16:0, DHCer18:0, DHCer18:1, DHCer24:0, DHCer24:1, gluCer22:0, lacCer16:0, phySph, S1P17:0, S1P18:0, SM18:0, sph16:1, sph17:0, sph18:0, sph18:1 or Sph20:1.

3. The use of claim 1, wherein the reagent is a reagent for detecting biomarker concentration levels.

4. The use of claim 3, wherein the reagent comprises a standard solution of the biomarker and an internal standard solution of the biomarker labeled with an isotope.

5. The use according to claim 4, wherein the isotopic labelling is ² H or ¹³ C。

6. The use according to claim 1, wherein the kit further comprises an extract comprising methanol and chloroform in a volume ratio of: 1:1-5:1.

7. the use of claim 6, wherein the kit further comprises 700 μl 96 well plates, 350 μl V-96 well plates, shrouded silica gel, 96 well sealed aluminum membranes.

8. The use according to any one of claims 1 to 7, wherein the kit is used in the following manner: determining the concentration level of the biomarker in the fecal sample of the subject, and inputting the determined values into a random forest model to obtain cut-off values for determination.

9. The use according to claim 8, wherein the method of using the kit comprises the steps of:

a) Preparing metabolite standard substance solutions with different concentrations;

b) Preparing an internal standard substance solution;

c) Preparing a fecal sample from a subject;

d) Measuring the sample prepared in c) by liquid chromatography and mass spectrometry, and calculating the concentration of the metabolite in the sample;

e) Inputting the concentration of the biomarker calculated in the step d) into a random forest model for calculation, and judging whether the subject is type II diabetes according to the score.