Metabolic marker combination, detection kit and detection method thereof, and auxiliary diagnosis method for female prediabetes mellitus

Abstract

The invention relates to the technical field of clinical medicine, in particular to a metabolic marker combination, a detection kit and a detection method thereof and an auxiliary diagnosis method for female prediabetes mellitus. The metabolic marker combination is composed of Cer, Cer d18: 0 / 16: 0, Cer d18: 1 / 18: 0, Cer d18: 1 / 20: 0, Cer d18: 0 / 22: 0, Cer d18: 1 / 24: 0 and Cer d18: 0 / 24: 0. According to the invention, a group of small molecule metabolites is preferably selected. The group of small molecule metabolites are bioactive metabolites which are closely related to insulin resistance, impaired glucose tolerance and chronic inflammation, and are related to pathogenesis of prediabetes mellitus. The metabolic marker combination and the total clinical indexes are comprehensively used for assisting in diagnosing the prediabetes occurrence risk, the effect is good, and the prediabetes diagnosis performance of female people is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of clinical medical technology, and in particular to a metabolic marker combination and a detection kit and a detection method thereof, and an auxiliary diagnosis method for female prediabetes. Background Art

[0002] According to the diagnostic criteria of the World Health Organization (WHO 1999), prediabetic mellitus (PreDM) refers to a state in which blood glucose levels are between normal and diabetes, including impaired fasting glucose, impaired glucose tolerance, and impaired fasting glucose combined with impaired glucose tolerance. Prediabetes can be considered a sign or a watershed, and its presence indicates an increased risk of diabetes, cardiovascular and cerebrovascular diseases, microvascular diseases, tumors, dementia and other diseases in the future. Prediabetes can develop for several years in individuals diagnosed with type 2 diabetes (T2DM), and early diagnosis and treatment can effectively prevent the occurrence and development of diabetes. Many studies have shown that T2DM can be prevented or delayed through enhanced behavioral lifestyle and drug interventions in high-risk groups, especially in subjects with PreDM.

[0003] Increases in blood pressure, low-density lipoprotein cholesterol, and HbA1c in perimenopausal women often occur in parallel with adverse changes in body fat distribution, especially women with polycystic disease and higher testosterone levels who are at a higher risk of developing T2DM. However, existing technologies that use lipid combined markers to diagnose PreDM do not take into account the impact of the above factors on prediabetes.

[0004] Based on this, it is necessary to provide a more reliable way to diagnose prediabetes in women, so as to better help and guide patients to receive early treatment. Summary of the invention

[0005] In order to solve the technical problem of the current lack of diagnostic methods for prediabetes in women, the present invention provides a metabolic marker combination and a detection kit and a detection method thereof, and an auxiliary diagnostic method for prediabetes in women.

[0006] In a first aspect, the present invention provides a metabolic marker combination for assisting the diagnosis of prediabetes in women, consisting of Cer, Cer d18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cer d18:0 / 22:0, Cer d18:1 / 24:0 and Cer d18:0 / 24:0.

[0007] In a second aspect, the present invention provides a detection kit for the above-mentioned metabolic marker combination, comprising: Standard: Cer d18:1 / 17:0, used for qualitative and quantitative detection of Cer, Cer d18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cer d18:0 / 22:0, Cer d18:1 / 24:0, Cer d18:0 / 24:0 in serum samples; Extraction solvent: methanol solution containing internal standard Cer d18:1 / 17:0, methyl tert-butyl ether and ultrapure water.

[0008] Furthermore, the internal standard concentration of the extractant was 0.2~0.5 µg / mL.

[0009] In a third aspect, the present invention provides a method for detecting the above-mentioned metabolic marker combination, comprising at least the following steps: S1: Ultra-high performance liquid chromatography-high resolution mass spectrometry was used to separate and detect the pretreated serum sample, which contained the internal standard Cer d18:1 / 17:0; S2: Cer, Cer d18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cer d18:0 / 22:0, Cer d18:1 / 24:0, and Cer d18:0 / 24:0 in serum samples were qualitatively identified based on the retention time of Cer d18:1 / 17:0 standards and the secondary fragment information, and the chromatographic peak intensities were extracted; S3: Compare the chromatographic peak intensities of Cer, Cer d18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cerd18:0 / 22:0, Cer d18:1 / 24:0, and Cer d18:0 / 24:0 in serum samples with those of the internal standard Cer d18:1 / 17:0 in serum samples to obtain the relative concentration of each ceramide.

[0010] Furthermore, the pretreatment method of serum samples is: (1) The serum samples were extracted with an extractant containing an internal standard, which was a mixed system of methanol, methyl tert-butyl ether and ultrapure water containing the internal standard Cer d18:1 / 17:0, where the volume ratio of methanol, methyl tert-butyl ether and ultrapure water was 1:2~4:1, and the internal standard concentration was 0.2~0.5 µg / mL; (2) Centrifuge the extracted product, freeze-dry the upper layer of solution and set aside for later use, then re-dissolve it before testing.

[0011] Furthermore, the re-dissolution in step (2) uses an acetonitrile / isopropanol / water system containing ammonium acetate, the volume ratio of acetonitrile / isopropanol / water is 65:30:5, and the concentration of ammonium acetate is 10 mM.

[0012] Furthermore, the elution solvent used in the liquid chromatography in the ultra-high performance liquid chromatography-high resolution mass spectrometry separation and detection includes mobile phase A and mobile phase B. Mobile phase A is an acetonitrile / water system containing ammonium acetate, and the volume ratio of acetonitrile / water is 6:4. Mobile phase B is an isopropanol / water system containing ammonium acetate, and the volume ratio of isopropanol / water is 9:1. The concentration of ammonium acetate in mobile phase A and mobile phase B is 10 mM.

[0013] Furthermore, in the ultra-high performance liquid chromatography-high resolution mass spectrometry separation and detection, the liquid chromatography used a reversed phase chromatography C8 column (2.1 × 100 mm, 1.7 µm), the column temperature was 55 °C, and the column flow rate was 0.26 mL / min; The mass spectrometer was used in the electrospray ionization positive ion mode (ESI + ) for testing.

[0014] In a fourth aspect, the present invention provides an auxiliary diagnosis method for prediabetes in women, comprising the following steps: Ultra-performance liquid chromatography-high-resolution mass spectrometry was used to separate and detect serum samples of training set subjects used for diagnostic model construction to obtain the concentration levels of each ceramide in the metabolite marker combination, which consisted of Cer, Cerd18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cer d18:0 / 22:0, Cer d18:1 / 24:0, and Cerd18:0 / 24:0; SPSS software was used to perform binary logistic regression analysis on the concentration levels of each ceramide in the metabolic marker combination of the training set subjects, clinical risk indicators and clinical protective factors to obtain the ROC curve, and the point on the curve with the maximum sum of sensitivity and specificity was calculated. The Prob value corresponding to this point was the optimal cutoff value of the ROC curve; Ultra-high performance liquid chromatography-high resolution mass spectrometry is used to separate and detect serum samples of the test subjects. The concentration levels of each ceramide in the metabolic marker combination of the test subjects, clinical risk indicators and clinical protective factors are substituted into the binary logistic regression equation obtained in the previous step to calculate the y value. The probability that the test subject has prediabetes is determined according to the formula Prob=1 / (1+e^(-y)). When the probability that the test subject has prediabetes is ≥ the optimal cutoff value of the ROC curve, it indicates that the test subject has a risk of prediabetes. All steps of the auxiliary diagnosis method are implemented by devices such as computers.

[0015] Furthermore, clinical risk indicators include age (Age), body mass index (BMI), waist-to-hip ratio (WHR), and triglycerides (TG); The clinical protective factor is high-density lipoprotein (HDL).

[0016] The beneficial effects of the present invention are: The present invention uses ultra-high performance liquid chromatography-high resolution mass spectrometry to detect serum metabolism and lipid spectrum, and selects a group of small molecule metabolites (Cer, Cer d18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cer d18:0 / 22:0, Cer d18:1 / 24:0 and Cer d18:0 / 24:0). This group of small molecule metabolites is a biologically active metabolite closely related to insulin resistance, impaired glucose tolerance and chronic inflammation, and is related to the pathogenesis of prediabetes. There is currently no report on the use of this group of metabolites for auxiliary diagnosis of prediabetes. The metabolic marker combination of the present invention and the total clinical index are used in combination to assist in the diagnosis of the risk of prediabetes, which has a good effect and greatly improves the diagnostic performance of prediabetes in the female population. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 : It is a comparison diagram of the distribution of the concentration of metabolic marker combinations in the serum of prediabetic patients and the control group; wherein, (A) is a comparison diagram of the distribution of Cer d18:0 / 16:0 concentrations in the serum of prediabetic patients and the control group; (B) is a comparison diagram of the distribution of Cer d18:1 / 18:0 concentrations in the serum of prediabetic patients and the control group; (C) is a comparison diagram of the distribution of Cer d18:1 / 20:0 concentrations in the serum of prediabetic patients and the control group; (D) is a comparison diagram of the distribution of Cer d18:0 / 22:0 concentrations in the serum of prediabetic patients and the control group; (E) is a comparison diagram of the distribution of Cer d18:0 / 24:0 concentrations in the serum of prediabetic patients and the control group; (F) is a comparison diagram of the distribution of Cer d18:1 / 24:0 concentrations in the serum of prediabetic patients and the control group; (G) is a comparison diagram of the distribution of total Cer concentrations in the serum of prediabetic patients and the control group.

[0019] Figure 2 This is the ROC curve diagram of age used to assist in the diagnosis of the risk of prediabetes.

[0020] Figure 3 This is the ROC curve of body mass index for assisting in the diagnosis of the risk of prediabetes.

[0021] Figure 4 This is the ROC curve of waist-to-hip ratio for assisting in diagnosing the risk of prediabetes.

[0022] Figure 5 This is the ROC curve of triglycerides used to assist in the diagnosis of the risk of prediabetes.

[0023] Figure 6 This is the ROC curve of high-density lipoprotein for assisting in the diagnosis of the risk of prediabetes.

[0024] Figure 7 It is the ROC curve diagram of total clinical indicators used to assist in the diagnosis of the risk of prediabetes.

[0025] Figure 8 It is a ROC curve diagram of the metabolic marker combination and the total clinical index for auxiliary diagnosis of the risk of prediabetes.

[0026] Fig. 9 yes Figure 1 to Figure 8 Comparison chart of ROC curves. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] Example 1 1. Collection of serum samples The volunteers were perimenopausal women, 121 of whom were diagnosed with prediabetes, and 229 of whom were not diagnosed with prediabetes as a control group. All volunteers included in the study signed an informed consent form before blood sample collection.

[0029] Serum samples from volunteers were collected under the same conditions and placed in a -80°C refrigerator.

[0030] 2. Analysis methods 2.1 Serum sample pretreatment Serum samples were thawed at 4°C, 40 µL of serum was taken into a 2 mL EP tube, 300 µL of methanol solution containing 0.3 µg / mL internal standard Cer d18:1 / 17:0 was added, vortexed, 1 mL of methyl tert-butyl ether was added, vortexed for 10 min, 300 µL of ultrapure water was added, vortexed, allowed to stand, centrifuged (14000 rpm×10 min, 4°C), 400 µL of the supernatant was lyophilized, and stored in a -80°C refrigerator for mass spectrometry analysis.

[0031] 2.2 Ultra-high performance liquid chromatography-high resolution mass spectrometry separation and detection (1) Liquid phase conditions: The chromatograph was a Shimadzu ultra-high performance liquid phase system, and the chromatographic column was a Waters UPLC BEH C8 column (2.1 mm×100 mm, 1.7 µm). Mobile phase A was acetonitrile / water (6:4, v / v), and mobile phase B was isopropanol / water (9:1, v / v), both containing 10 mM ammonium acetate. The column temperature was 55°C, the flow rate was 0.26 mL / min, and the injection volume was 5 µL. The elution program was as follows: 0-1.5 min, 68% A; 1.5 min-15 min, 68%-15% A; 15 min-15.6 min, 15%-3% A; 15.6-18 min, 3% A; 18 min-18.1 min, 3%-68% A; 18.1 min-20 min, 68% A. (2) Mass spectrometry conditions: Mass spectrometer: AB SCIEX Triple Q-TOF5600 + The system (Concord, Canada) was used for analysis in positive ion mode, with an ion source voltage of 5500 V, a surface heating temperature of 550°C, ion source gas 1 and gas 2 of 55 psi, and a curtain gas of 35 psi. The mass spectrometer scan range was 300-1250 m / z.

[0032] 3. Sample analysis 3.1 Analysis of standard products: The Cer d18:1 / 17:0 standard was prepared in a 1:2 (v / v) methanol-chloroform mixture with a concentration of 0.33 µg / mL. The standard solution was separated and detected by ultra-high performance liquid chromatography-high resolution mass spectrometry. The retention time of Cer d18:1 / 17:0 and the corresponding secondary information were confirmed based on the mass-to-charge ratio of Cer d18:1 / 17:0 extraction.

[0033] 3.2 Serum sample analysis: Serum samples were redissolved in an acetonitrile / isopropanol / water system containing ammonium acetate. The volume ratio of acetonitrile / isopropanol / water was 65:30:5, and the concentration of ammonium acetate was 10 mM. The serum sample solution was separated and detected by ultra-high performance liquid chromatography-high-resolution mass spectrometry. According to the retention time of the standard and the secondary fragment information, Cer d18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cer d18:0 / 22:0, Cer d18:0 / 24:0, Cer d18:1 / 24:0, and Cer in the serum samples were qualitatively identified, and the chromatographic peak intensity was extracted.

[0034] Figure 1 (A) to (G) are comparison diagrams of the distribution of concentrations of seven ceramide metabolites, namely Cer d18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cer d18:0 / 22:0, Cer d18:0 / 24:0, Cer d18:1 / 24:0, and Cer in the serum of prediabetic (Predm) volunteers and the control group (Nc). It can be seen that the levels of Cer d18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cer d18:0 / 22:0, Cerd18:0 / 24:0, Cer d18:1 / 24:0, and Cer in the baseline serum of prediabetic volunteers were significantly increased.

[0035] SPSS software was used to perform binary logistic regression analysis on each single clinical risk index (age, BMI, WHR, TG), clinical protective factor (HDL), total clinical index (Age, BMI, WHR, TG, HDL), and the combination of metabolic markers and total clinical index to obtain ROC curves. The results are as follows: Figures 2~9 As shown, the ordinate of the ROC curve is sensitivity, and the abscissa is (1-specificity).

[0036] Figure 2 The AUC of the ROC curve was 0.607, the sensitivity was 0.736, and the specificity was 0.445.

[0037] Figure 3 The AUC of the ROC curve was 0.608, the sensitivity was 0.347, and the specificity was 0.86.

[0038] Figure 4 The AUC of the ROC curve was 0.326, the sensitivity was 0.991, and the specificity was 0.014.

[0039] Figure 5The AUC of the ROC curve was 0.668, the sensitivity was 0.636, and the specificity was 0.616.

[0040] Figure 6 The AUC of the ROC curve was 0.548, the sensitivity was 0.446, and the specificity was 0.668.

[0041] Figure 7 The AUC of the ROC curve was 0.668, the sensitivity was 0.636, and the specificity was 0.616.

[0042] Figure 8 The AUC of the ROC curve was 0.768, the sensitivity was 0.728, and the specificity was 0.685. The binary logistic regression equation was as follows: y=0.193×age (year)+0.033×BMI (kg / m 2 )+0.036×Cer d18:0 / 16:0 (μg / mL)+0.268×Cer d18:1 / 18:0 (μg / mL)+0.324×Cer d18:1 / 20:0 (μg / mL)+0.132×Cer d18:0 / 22:0 (μg / mL)+0.640×Cer d18:1 / 24:0 (μg / mL)+1.13×Cer d18:0 / 24:0 (μg / mL)-1.352×Cer (μg / mL)-0.75×HDL (mmol / L)-0.095×WHR-0.559×TG (mmol / L); The probability of having prediabetes in the female population is Prob=1 / (1+e^(-y)).

[0043] The point on the curve where the sum of sensitivity and specificity is the largest is calculated, and the Prob value corresponding to this point is the optimal cut-off value. The optimal cut-off value of the combined marker determined in this embodiment for whether prediabetes occurs in the female population is 0.294, and the sensitivity and specificity corresponding to this point are the sensitivity and specificity of the marker for auxiliary diagnosis of the disease. For the female population, if the Prob value is greater than or equal to the optimal cut-off value, it is judged that the patient may have prediabetes, and if it is less than the optimal cut-off value, it indicates that the probability of having prediabetes is extremely low. In other embodiments, a new equation can also be obtained through binary logistic regression based on the experimental results of different volunteers (training set subjects), and the corresponding optimal cut-off value can be determined.

[0044] Depend on Fig. 9It can be seen that the model constructed by combining the seven ceramides (Cer, Cer d18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cer d18:0 / 22:0, Cer d18:1 / 24:0, Cer d18:0 / 24:0) screened in the present invention with the total clinical indicators (Age, BMI, WHR, TG, HDL) has a good auxiliary diagnosis ability for whether or not a person has prediabetes. The excellent ROC curve results and good diagnostic accuracy make this combined marker have the potential for auxiliary diagnosis of prediabetes.

[0045] Example 2 A detection kit for a combination of metabolic markers for assisting in the diagnosis of prediabetes in women. The metabolic marker panel consisted of Cer, Cer d18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cer d18:0 / 22:0, Cer d18:1 / 24:0, and Cer d18:0 / 24:0; The test kit includes: Standard: Cer d18:1 / 17:0, used for qualitative and quantitative detection of Cer, Cer d18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cer d18:0 / 22:0, Cer d18:1 / 24:0, Cer d18:0 / 24:0 in serum samples; Extraction agent: methanol solution containing internal standard Cer d18:1 / 17:0, methyl tert-butyl ether and ultrapure water, the internal standard concentration is 0.2~0.5 µg / mL.

[0046] Although the present invention has been described in detail with reference to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention.

Claims

1. A metabolic marker combination for assisting the diagnosis of prediabetes in women, characterized in that: Consists of Cer, Cerd18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cer d18:0 / 22:0, Cer d18:1 / 24:0 and Cerd18:0 / 24:

0.

2. A detection kit for the metabolic marker combination according to claim 1, characterized in that: include: Standard: Cer d18:1 / 17:0, used for qualitative and quantitative detection of Cer, Cer d18:0 / 16:0, Cer d18:1 / 18:0, Cerd18:1 / 20:0, Cer d18:0 / 22:0, Cer d18:1 / 24:0, Cer d18:0 / 24:0 in serum samples; Extraction solvent: methanol solution containing internal standard Cer d18:1 / 17:0, methyl tert-butyl ether and ultrapure water.

3. The detection kit according to claim 2, characterized in that The internal standard concentration of the extractant was 0.2~0.5 µg / mL.

4. A method for detecting a combination of metabolic markers as claimed in claim 1, characterized in that: At least the following steps are included: S1: Ultra-high performance liquid chromatography-high resolution mass spectrometry was used to separate and detect the pretreated serum sample, which contained the internal standard Cer d18:1 / 17:0; S2: Cer, Cer d18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cer d18:0 / 22:0, Cer d18:1 / 24:0, and Cer d18:0 / 24:0 in serum samples were qualitatively identified based on the retention time of Cer d18:1 / 17:0 standards and the secondary fragment information, and the chromatographic peak intensities were extracted; S3: Compare the chromatographic peak intensities of Cer, Cer d18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cerd18:0 / 22:0, Cer d18:1 / 24:0, and Cer d18:0 / 24:0 in serum samples with those of the internal standard Cer d18:1 / 17:0 in serum samples to obtain the relative concentration of each ceramide.

5. The detection method according to claim 4, characterized in that: The pretreatment method of serum samples was as follows: (1) The serum samples were extracted with an extractant containing an internal standard, which was a mixed system of methanol, methyl tert-butyl ether and ultrapure water containing the internal standard Cer d18:1 / 17:0, where the volume ratio of methanol, methyl tert-butyl ether and ultrapure water was 1:2~4:1, and the internal standard concentration was 0.2~0.5 µg / mL; (2) Centrifuge the extracted product, freeze-dry the upper layer of solution and set aside for later use, then re-dissolve it before testing.

6. The detection method according to claim 5, characterized in that In step (2), the reconstitution is carried out using an acetonitrile / isopropanol / water system containing ammonium acetate, the volume ratio of acetonitrile / isopropanol / water is 65:30:5, and the concentration of ammonium acetate is 10 mM.

7. The detection method according to claim 4, characterized in that The elution solvents used in liquid chromatography in ultra-high performance liquid chromatography-high resolution mass spectrometry separation and detection include mobile phase A and mobile phase B. Mobile phase A is an acetonitrile / water system containing ammonium acetate, and the volume ratio of acetonitrile / water is 6:

4. Mobile phase B is an isopropanol / water system containing ammonium acetate, and the volume ratio of isopropanol / water is 9:

1. The concentrations of ammonium acetate in mobile phase A and mobile phase B are both 10 mM.

8. The detection method according to claim 4, characterized in that In the ultra-high performance liquid chromatography-high resolution mass spectrometry separation and detection, the liquid chromatography used a reversed phase chromatography C8 column, the column temperature was 55 °C, and the column flow rate was 0.26 mL / min; In ultra-high performance liquid chromatography-high resolution mass spectrometry separation and detection, mass spectrometry was detected in electrospray ionization positive ion mode.

9. An auxiliary diagnosis method for prediabetes in women, characterized in that: The steps include: Ultra-performance liquid chromatography-high-resolution mass spectrometry was used to separate and detect serum samples of training set subjects used for diagnostic model construction to obtain the concentration levels of each ceramide in the metabolite marker combination, which consisted of Cer, Cer d18:0 / 16:0, Cer d18:1 / 18:0, Cer d18:1 / 20:0, Cer d18:0 / 22:0, Cer d18:1 / 24:0, and Cer d18:0 / 24:0; SPSS software was used to perform binary logistic regression analysis on the concentration levels of each ceramide in the metabolic marker combination of the training set subjects, clinical risk indicators and clinical protective factors to obtain the ROC curve, and the point on the curve with the maximum sum of sensitivity and specificity was calculated. The Prob value corresponding to this point was the optimal cutoff value of the ROC curve; Ultra-high performance liquid chromatography-high-resolution mass spectrometry was used to separate and detect the serum samples of the test subjects. The concentration levels of each ceramide in the metabolic marker combination of the test subjects, clinical risk indicators and clinical protective factors were substituted into the binary logistic regression equation obtained in the previous analysis to calculate the y value. The probability that the test subject had prediabetes was determined according to the formula Prob=1 / (1+e^(-y)). When the probability of the test subject having prediabetes was ≥ the optimal cutoff value of the ROC curve, it indicated that the test subject was at risk of prediabetes.

10. The auxiliary diagnosis method according to claim 9, characterized in that: Clinical risk indicators included age, body mass index, waist-to-hip ratio, and triglycerides; The clinical protective factor is high-density lipoprotein.

Images