Use of polyunsaturated fatty acid combined marker in preparation of detection product for diagnosing diabetes

Abstract

Disclosed in the present invention is a use of a polyunsaturated fatty acid combined marker in the preparation of a detection product for diagnosing diabetes. The polyunsaturated fatty acid combined marker is selected from FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:3. Statistics are collected by means of ROC curves after the concentration level of the polyunsaturated fatty acid combined marker in the serum of a subject is measured, the prediction value of the combined marker for the risk of type 2 diabetes is determined by means of the area under the ROC curve, and the polyunsaturated fatty acid combined marker is used for assisting in the early clinical diagnosis of diabetes, has high sensitivity, low costs and good repeatability, and achieves a synergistic effect with fasting blood glucose fasting blood sugar and postprandial blood glucose.

Description

Application of polyunsaturated fatty acid combined markers in the preparation of test products for diagnosing diabetes Technical Field

[0001] The present invention belongs to the field of biomedical testing, and in particular relates to the application of a polyunsaturated fatty acid combined marker in the preparation of a test product for diagnosing diabetes. Background Art

[0002] With the development of social economy, the diet and lifestyle habits of the Chinese people have changed significantly. The increased intake of Western-style diets characterized by high oil and fat, the decrease in physical activity, and the increase in sedentary time have led to a continuous increase in the incidence of chronic metabolic diseases such as diabetes and hyperlipidemia in my country. Metabolic diseases, as diseases caused by metabolic disorders in the human body, are diverse. Among them, type 2 diabetes, obesity, metabolic syndrome, and non-alcoholic fatty liver disease have become major global health problems. Therefore, the prevention and treatment of metabolic diseases is urgent. In recent years, the rapid development of metabolomics technology has made it possible to study the detailed metabolic profile of diabetes and provided important methods and approaches for in-depth analysis of the occurrence and development of diseases.

[0003] Type 2 diabetes is one of the most important clinical manifestations of the interaction of glucose and lipid metabolism disorders. Its clinical characteristics are hyperglycemia, insulin resistance and impaired insulin secretion. At the same time, about 60%-70% of patients with type 2 diabetes will have abnormal lipid metabolism. At present, the diagnosis of diabetes is mainly based on the level of plasma glucose. However, since blood glucose testing is easily affected by factors such as drugs, diet, and emotions, and fluctuates greatly, its diagnosis has certain limitations. In addition, the onset of early diabetes is hidden, and typical clinical symptoms are absent or absent. Once discovered, it is often too late. Therefore, the development of new diagnostic tests with potential to predict the occurrence of early diabetes is of great significance for reducing the morbidity and mortality of diabetes.

[0004] Fatty acids found in natural foods are classified as SFAs, MUFAs, and PUFAs. PUFAs are further divided into n-6 PUFAs and n-3 PUFAs. Due to their distinct chemical structures and physical properties, they have different health effects. Studies have shown that a high n-6 / n-3 fatty acid ratio is associated with the development of certain diseases, including cardiovascular disease, cancer, inflammation, and autoimmune diseases. Increasing n-3 PUFA intake, thereby reducing the n-6 / n-3 fatty acid ratio, can reduce the risk of these diseases. This suggests that an appropriate dietary n-6 / n-3 PUFA ratio is an effective approach to reducing the risk of cardiovascular disease and other inflammatory diseases. Furthermore, ω3 and ω6 fatty acids are metabolically and physiologically distinct PUFA isoforms with distinct roles in body fat gain and the development of obesity. Dietary ω3 and ω6 fatty acids, the ω6:ω3 ratio, and their biomarkers have been shown to correlate with longitudinal body fat and obesity indicators. However, there are currently no reports on the use of polyunsaturated fatty acids as biomarkers for detecting new-onset diabetes.

[0005] Summary of the Invention

[0006] The purpose of the present invention is to provide an application of a polyunsaturated fatty acid combined marker in the preparation of a detection product for diagnosing diabetes, thereby providing a new auxiliary detection method for clinical screening of early diabetes.

[0007] The above-mentioned purpose of the present invention is achieved through the following technical solutions:

[0008] The present invention provides the use of a polyunsaturated fatty acid combined marker in preparing a detection reagent for diagnosing diabetes, wherein the polyunsaturated fatty acid combined marker is composed of any two or more of FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:3.

[0009] Preferably, the polyunsaturated fatty acid joint marker consists of FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:3.

[0010] Preferably, the diabetes is type 2 diabetes.

[0011] Preferably, the detection product is a blood detection reagent.

[0012] The present invention also provides the use of a reagent for detecting a polyunsaturated fatty acid combined marker in the preparation of a detection product for diagnosing diabetes, wherein the polyunsaturated fatty acid combined marker consists of FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:3.

[0013] Preferably, the detection product is a kit, which contains FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:3, as standard substances for the qualitative analysis of the corresponding serum metabolites FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:3, respectively.

[0014] The present invention also provides a method for assessing the risk of early-stage type 2 diabetes, comprising the following steps:

[0015] Step 1: Detecting the concentration level of polyunsaturated fatty acid combined markers in the serum of the subject by ultra-performance liquid chromatography-quadrupole electrostatic field orbitrap high-resolution mass spectrometry; the polyunsaturated fatty acid combined markers consist of FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:3;

[0016] Step 2: Perform ROC curve statistics based on the concentration level of the polyunsaturated fatty acid combined marker in the subject's serum, and judge the predictive value of the polyunsaturated fatty acid combined marker for the risk of type 2 diabetes by the area under the ROC curve; when the area under the ROC curve is greater than 0.7, it indicates that the polyunsaturated fatty acid combined marker has a good predictive effect on the risk of type 2 diabetes.

[0017] Compared with the prior art, the present invention has the beneficial effect that the polyunsaturated fatty acid combined marker can be used to predict the occurrence of type 2 diabetes, providing a new auxiliary detection method and theoretical basis for clinical screening of early diabetes. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] FIG1 is a ROC curve diagram of the discovery group of polyunsaturated fatty acid combined markers for diagnosing newly diagnosed diabetes in the examples.

[0019] Figure 2 is the ROC curve of the validation group Jilin Center polyunsaturated fatty acid combined markers for the diagnosis of new-onset diabetes.

[0020] Figure 3 is the ROC curve of the validation group Guiyang Center polyunsaturated fatty acid combined markers for the diagnosis of new-onset diabetes.

[0021] Figure 4 is the ROC curve of the Guangzhou Center polyunsaturated fatty acid combined markers for the diagnosis of new-onset diabetes in the validation group. DETAILED DESCRIPTION

[0022] Below in conjunction with embodiment, the present invention is further described:

[0023] The definition of diabetes mellitus was as follows: fasting plasma glucose (FPG) ≥ 126 mg / dL or 2-h postprandial plasma glucose (2-h PG) ≥ 200 mg / dL, or the subjects reported that they had been diagnosed with diabetes by a professional physician and were taking hypoglycemic drugs.

[0024] Example 1

[0025] The researchers first analyzed serum fatty acids in a discovery population (n=106) using a Q Exactive quadrupole mass spectrometer and liquid chromatography-mass spectrometry (LC-MS). After adjusting for relevant confounding factors (including age, sex, BMI, FPG, smoking and drinking status, family history of diabetes, education level, physical activity, and 2hPG), they found that 11 polyunsaturated fatty acids were statistically significantly associated with the occurrence of diabetes: FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:3.

[0026] The results were further verified in populations (n=386) from three verification centers in different provinces across the country (Jilin, Guiyang and Guangdong), and it was found that the above 11 polyunsaturated fatty acids still had a significant correlation with the risk of diabetes, as shown in Figures 2-4.

[0027] Then, the ROC model showed that compared with the traditional diagnostic markers FPG and 2-h PG, when the above-mentioned polyunsaturated fatty acid combined markers were added, its diagnostic sensitivity and specificity for type 2 diabetes were significantly increased, and the area under the curve increased from 0.6796 to 0.8216, as shown in Figure 1.

[0028] The specific operation process is as follows:

[0029] (1) Serum sample collection and processing

[0030] All volunteers enrolled in the study signed informed consent before serum samples were collected. Blood samples were collected from 106 (discovery group) and 386 (validation group) subjects under identical conditions. After collection, serum was collected directly after 60 minutes of stagnation and stored at -80°C until further use.

[0031] (2) Research subjects

[0032] A case-control study was conducted. A total of 106 subjects with normal glucose tolerance (NGR) at baseline who met the criteria were enrolled in the discovery group from the national cohort 4C study population. These included 53 new-onset diabetes cases and 53 NGR subjects during follow-up. The baseline characteristics of the discovery group are shown in Table 1. A total of 386 subjects with NGR at baseline were enrolled in the validation group from the 4C cohort population. These included 193 new-onset diabetes cases and 193 subjects who remained NGR.

[0033] Inclusion criteria: 1) subjects aged ≥40 years, 2) underwent oral glucose tolerance test (OGTT).

[0034] Exclusion criteria: patients with diabetes or impaired glucose regulation at baseline.

[0035] In addition, all participants underwent a standard questionnaire and physical examination. FPG was measured in fasting venous plasma samples, and 2-h PG was measured in 2-h venous plasma samples after oral glucose tolerance test (OGTT). FPG and 2-h PG concentrations were measured using an ADVIA-1650 chemistry automated analyzer (Bayer Diagnostics, Tarrytown, NY, USA).

[0036] Table 1: Baseline characteristics of the discovery cohort

[0037] Table 1 shows that the two groups were well matched at baseline, with no statistically significant differences in baseline characteristics such as age, sex, BMI, family history of diabetes, and 2-h PG between the two groups. Only alcohol consumption and FPG were significantly different in diabetic patients compared with NGR patients at baseline.

[0038] (3) Fatty acid omics analysis

[0039] 1. Reagents

[0040] FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:3 were all purchased from Tianjin Alta Technology Co., Ltd.; methanol and acetonitrile were purchased from Merk, Germany; isopropanol and dichloromethane were purchased from Sigma-Aldrich; methyl tert-butyl ether (MTBE) was purchased from Spectrum Chemical; and chloroform was purchased from Longxi Science Co., Ltd.

[0041] 2. Sample pretreatment

[0042] A 30 μL blood sample was collected and 180 μL of methanol containing internal standards (1.5 μg / mL for FA 12:0-d23, 1.5 μg / mL for FA 16:0-d3, 1.5 μg / mL for FA 20:0-d3, and 0.375 μg / mL for FA 24:0-d47) was added to extract the target compounds and precipitate the proteins. The sample was vortexed, and 600 μL of MTBE was added, followed by 150 μL of ultrapure water. The sample was shaken for 10 minutes and centrifuged. The supernatant was aspirated and dried with nitrogen. Prior to instrumental analysis, 20 μL of solution A (chloroform / methanol (v:v = 2:1)) was added, followed by 40 μL of solution B (acetonitrile / isopropanol / MilliQ water (v:v:v = 65:30:5). The sample was vortexed, centrifuged, and the supernatant was analyzed.

[0043] 3. Instrumental methods

[0044] The instrument used was Vanquish UHPLC-Q Exactive (Thermo Fisher Scientific, Rockford, IL, USA).

[0045] The chromatographic column was an ACQUITY UPLC C8 column (100 × 2.1 mm, 1.7 μm). The column temperature was 55°C, the flow rate was 0.2 mL / min, the injection chamber temperature was 10°C, and the injection volume was 5 L. Mobile phase A consisted of 25% acetonitrile / MilliQ water (containing 10 mM ammonium acetate), and mobile phase B consisted of 20% / 75% / 5% acetonitrile / isopropanol / water (containing 10 mM ammonium acetate). Mobile phase gradient conditions were as follows: mobile phase B started at 15% and was maintained for 1.5 min; then increased to 100% over the next 0.5 min and maintained for 6 min; then returned to 15% over the next 0.5 min and equilibrated for 1.5 min.

[0046] The sheath gas flow rate of the mass spectrometer ion source was 45 arb, the auxiliary gas flow rate was 10 arb, the spray voltage was 3.0 kV (ESI-), the capillary temperature was 320 °C, the auxiliary gas temperature was 350 °C, and the resolution was 7e4 , using full scan mode, the scanning range was 70-600 m / z.

[0047] 4. Experimental part

[0048] (1) Data analysis stability

[0049] In order to monitor the stability of the instrument, mixed samples are used as quality control samples. The pretreatment process of the quality control samples is the same as that of the actual samples. One quality control sample is run for every 10 samples. Three QCs are run each time the instrument is cleaned to check the stability of the instrument. After replacing a new chromatographic column or cleaning the instrument and balancing it, a linear curve is run to ensure quantitative accuracy. The RSD% of the fatty acid detection values ​​in most quality control samples is less than 30%, which shows that the instrument is stable and in good condition.

[0050] (2) Detection limit, quantification limit and linear curve

[0051] Accurately weigh and prepare fatty acid mother solution with chloroform / methanol solution. According to the actual sample content and instrument sensitivity, select the linear range to draw the linear curve. The lowest point of the linear range is the limit of quantification. Use concentration as the horizontal axis and the ratio of each fatty acid peak area to the corresponding internal standard as the vertical axis to obtain the linear equation. The linear correlation coefficient of fatty acid R 2 All are greater than 0.99.

[0052] The measured fatty acids were evaluated for the risk of new-onset type 2 diabetes using a multivariate logistic regression equation. The results showed that after adjusting for age, sex, body mass index, smoking, drinking, physical activity, education level, family history of diabetes, FPG and 2-hPG, 11 polyunsaturated fatty acid classification indicators were significantly associated with an increased risk of type 2 diabetes: FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:3.

[0053] The data statistical software SAS was used to further classify 11 polyunsaturated fatty acids as fatty acid classification indicators, and the ROC curve was used to determine their detection effect on type 2 diabetes. The results are shown in Figures 1-4.

[0054] In the discovery group, polyunsaturated fatty acids significantly improved the predictive value of type 2 diabetes risk compared with traditional risk factors and FPG and 2-h PG (Table 2).

[0055] Table 2: ROC comparison estimation and row-by-row test results

[0056] In Figure 1, the results of the discovery group showed that compared with the area under the ROC curve of 0.6796 for traditional risk factors and FPG and 2-h PG in predicting the risk of diabetes, when the polyunsaturated fatty acid classification index was used to predict diabetes, the area under the ROC curve value increased to 0.8216.

[0057] In Figure 2, the area under the ROC curve of the Jilin center in the validation group increased from 0.6798 to 0.8141.

[0058] In Figure 3 , the area under the ROC curve of the validation group in Guiyang, Guizhou Province increased from 0.7311 to 0.7950.

[0059] In Figure 4 , the area under the ROC curve of the validation group at Guangzhou Zhongshan Center increased from 0.6910 to 0.7959.

[0060] The results showed that the polyunsaturated fatty acid combined marker of the present invention has good potential for diagnosing diabetes and exhibits good synergy with the clinical diagnostic indicators FPG and 2-h PG. This suggests that the polyunsaturated fatty acid combined marker composed of FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:3 can serve as a novel serum marker for type 2 diabetes, useful for early screening and diagnosis of diabetes, and providing an auxiliary detection approach for clinically assessing the risk of diabetes.

[0061] The above is a preferred embodiment of the present invention, but the present invention should not be limited to the contents disclosed in this embodiment. Therefore, any equivalent or modified implementations that do not depart from the spirit disclosed in the present invention fall within the scope of protection of the present invention.

Claims

1. Use of a polyunsaturated fatty acid combined marker in the preparation of a detection reagent for diagnosing diabetes, wherein the polyunsaturated fatty acid combined marker is composed of any two or more of FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:

3.

2. The use according to claim 1, characterized in that: The polyunsaturated fatty acid joint marker consists of FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:

3.

3. The use according to claim 1, characterized in that: The diabetes is type 2 diabetes.

4. The use according to claim 1, characterized in that: The detection reagent is a blood detection reagent.

5. Use of a reagent for detecting a polyunsaturated fatty acid combined marker in the preparation of a detection product for diagnosing diabetes, wherein the polyunsaturated fatty acid combined marker consists of FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:

3.

6. The use according to claim 5, characterized in that: The detection product is a test kit, which contains FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:3, as standard substances for the qualitative analysis of the corresponding serum metabolites FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:3, respectively.

7. A method for assessing the risk of early stage type 2 diabetes, characterized in that: The following steps are involved: Step 1: Detecting the concentration level of polyunsaturated fatty acid joint markers in the serum of the subject by ultra performance liquid chromatography-quadrupole electrostatic field orbital trap high resolution mass spectrometry; the polyunsaturated fatty acid joint markers consist of FA19:2, FA20:4, FA22:3, FA22:4, FA24:2, FA24:4, FA24:5, FA25:2, FA25:3, FA26:2, and FA26:3; Step 2: Perform ROC curve statistics based on the concentration level of the polyunsaturated fatty acid combined marker in the subject's serum, and judge the predictive value of the polyunsaturated fatty acid combined marker for the risk of type 2 diabetes by the area under the ROC curve; when the area under the ROC curve is greater than 0.7, it indicates that the polyunsaturated fatty acid combined marker has a good predictive effect on the risk of type 2 diabetes.

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