Application of oxidized lipid-based biomarker composition in early diagnosis of diabetic nephropathy

Abstract

The invention provides application of a biomarker composition based on oxidized lipid in early diagnosis of diabetic nephropathy, and relates to the technical field of biomarkers. The invention provides a biomarker composition which can be used for distinguishing healthy people, diabetic people and diabetic nephropathy people by using the content levels of plasma ARA, plasma 5-HETE and plasma 5-oxoETE for the first time. The invention further provides a combined factor diagnosis and prediction model constructed by the plasma ARA, the plasma 5-HETE and the plasma 5-oxoETE for the first time, and the model is high in diagnosis and prediction capability and has relatively high accuracy, sensitivity and specificity. The oxidized lipid biomarker composition provided by the invention has a wide application prospect in preparation of a kit for early diagnosis and detection of diabetic nephropathy.

Description

Technical Field

[0001] The present invention relates to the technical field of biomarkers, and in particular to a biomarker composition based on oxidized lipids and its application in the early diagnosis of diabetic nephropathy. Background Art

[0002] Diabetic nephropathy (DKD) is a serious microvascular complication of diabetes mellitus (DM). It is the main cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) worldwide and is significantly associated with higher cardiovascular morbidity and all-cause mortality. In 2021, 10.5% of the world's population (about 537 million) suffered from diabetes, and this figure is expected to rise to 12.2% (about 783 million) by 2045. DKD has gradually increased with the increase in the incidence of DM. The cumulative incidence of type 1 diabetes mellitus (T1DM) progressing to DKD after 20 to 25 years is 20% to 40%, while this proportion of patients with type 2 diabetes mellitus (T2DM) is about 30% to 40%. If DKD does not receive early and effective treatment intervention, it will gradually develop into the end-stage renal disease stage and require hemodialysis, peritoneal dialysis or kidney transplantation, which will result in higher medical expenses and low cure rates.

[0003] In clinical practice, it is very difficult to predict the development of DKD by monitoring blood glucose. Although changes in glomerular filtration rate (eGFR) are the gold standard for assessing renal function, they are usually not detectable before substantial renal damage occurs in DKD, and lack sensitivity and specificity. Increased urinary albumin excretion rate is widely used to diagnose DKD, but studies have shown that the progression of renal impairment in DM patients may not be accompanied by the appearance of proteinuria, and early progressive renal function decline may even precede the occurrence of microalbuminuria. At present, renal puncture biopsy is still the gold standard for the pathological diagnosis of DKD, but the biopsy process may cause hematuria or perinephric hematoma in patients, complicating the condition. The lack of suitable early detection methods is the main obstacle to the prevention and treatment of DKD. More sensitive and specific biomarkers are urgently needed in the clinic to assess the nature, severity and progression rate of DKD, so as to achieve early diagnosis and early intervention.

[0004] DKD patients often have severe lipid metabolism disorders. Lipid metabolism disorders are significantly associated with renal prognosis, cardiovascular prognosis and mortality in DKD patients. Although a large number of studies have confirmed that hyperglycemia and subsequent hemodynamic abnormalities caused by glucose metabolism disorders are the main pathogenesis of DKD, recent studies have found that the impact of lipid metabolism imbalance caused by hyperglycemia on the pathophysiological changes of DKD may exceed that of hyperglycemia itself. Lipid metabolism homeostasis imbalance characterized by lipid metabolism reprogramming is an important cause of the occurrence and development of DKD.

[0005] Oxidized lipids refer to unsaturated fatty acids (PUFAs), including arachidonic acid, linoleic acid, linolenic acid, hexacosahexaenoic acid and eicosapentaenoic acid, which are a series of oxidative metabolites generated in the liver by specific oxidative metabolic enzymes such as cyclooxygenase, lipoxygenase and cytochrome P450. The kidney is also an important local metabolic tissue organ for the metabolism of PUFAs to produce oxidized lipids. Oxidized lipids are closely related to the occurrence and development of diseases such as tumors, cardiovascular diseases, diabetes, asthma, nephritis and colitis. In the DKD disease state, the body's inflammatory state activates oxidized lipids to undergo lipid metabolism reprogramming, which in turn participates in the occurrence and development of the disease.

[0006] Lipid metabolic reprogramming refers to the body's readjustment of lipid metabolic pathways in response to stimulation from both the internal and external environments to adapt to the energy requirements and metabolic states under new pathophysiological conditions. This process not only involves changes in the expression levels of specific metabolic enzymes, but also includes the redirection of lipid metabolic pathways, which causes the accumulation or consumption of specific metabolites, ultimately leading to excessive accumulation of lipid peroxides. Due to the structural characteristics of cell membranes and mitochondrial membranes that are rich in phospholipids, oxidized lipids can easily enter the mitochondria through the cell membrane, inducing the accumulation of mitochondrial free electrons and the production of a large amount of reactive oxygen species. Imbalance in oxidized lipid metabolism can lead to changes in intracellular signaling pathways and epigenetic traits, thereby destroying cell structure and function. Oxidized lipids and their active metabolites that change during metabolic reprogramming are expected to become biomarkers for metabolic diseases and play an important role in the early diagnosis of diseases. Studies have shown that metabolomics-based unsaturated fatty acid metabolomics has great potential for early diagnosis of DKD. However, little is known about the association between changes in unsaturated fatty acid profiles and DKD diseases. Summary of the invention

[0007] 1. Technical issues to be solved

[0008] In view of the deficiencies of the prior art, the present invention provides a biomarker composition based on oxidized lipids and its application in the early diagnosis of diabetic nephropathy.

[0009] (II) Technical solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0011] In a first aspect, the present invention provides a biomarker composition based on oxidized lipids, wherein the biomarker composition consists of plasma arachidonic acid (ARA), plasma 5-hydroxyeicosatetraenoic acid (5-HETE), and plasma 5-oxoeicosatetraenoic acid (5-oxoETE).

[0012] In a second aspect, the present invention provides the use of the above biomarker combination in the preparation of a test kit for early diagnosis of diabetic nephropathy.

[0013] In the third aspect, the present invention provides a prediction model for the early diagnosis of diabetic nephropathy, wherein the prediction model uses plasma arachidonic acid (ARA), plasma 5-hydroxyeicosatetraenoic acid (5-HETE), and plasma 5-oxoeicosatetraenoic acid (5-oxoETE) as construction parameters, performs Logistics regression, and establishes a joint factor regression equation; the regression equation is: Logit(P)=0.001*Log(ARA)+0.076*Log(5-HETE)+0.570*Log(5-oxoETE)-11.895.

[0014] Furthermore, the Cut-off value of the above prediction model for diagnosing and distinguishing between healthy people and diabetic nephropathy patients is 0.626; when the Cut-off value is greater than or equal to 0.626, an early diagnosis prediction can be made for the patient.

[0015] (III) Beneficial effects

[0016] 1. The present invention proposes for the first time that the levels of plasma ARA, plasma 5-HETE and plasma 5-oxoETE can be used as a biomarker combination to distinguish healthy people, diabetic people and diabetic nephropathy people.

[0017] 2. The present invention is the first to construct a combined factor diagnostic prediction model using plasma ARA, plasma 5-HETE and plasma 5-oxoETE. Compared with the single oxidized lipid factor prediction model, this model has stronger diagnostic prediction ability for diabetic nephropathy, higher accuracy, sensitivity and specificity.

[0018] 3. The oxidized lipid biomarker composition provided by the present invention has broad application prospects in the preparation of an early diagnosis and detection kit for diabetic nephropathy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1Shown are the levels of plasma ARA (A), plasma 5-HETE (B), and plasma 5-oxoETE (C) in serum samples among the DKD, T2DM, and CON groups in the sample set; *P<0.05; **P<0.01; ***P<0.001.

[0020] Figure 2 The levels of plasma ARA (A), plasma 5-HETE (B), and plasma 5-oxoETE (C) in serum samples among DKD, T2DM, and CON groups in the validation set; *P<0.05; **P<0.01; ***P<0.001.

[0021] Figure 3 The diagnostic efficacy of the combined diagnostic model of plasma ARA, plasma 5-HETE, plasma 5-oxoETE and their three oxidized lipid factors for all patients in the DKD, T2DM and CON groups in the training and validation sets. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described in combination with the embodiments of the present invention. Obviously, the described embodiments are 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 are within the scope of protection of the present invention.

[0023] Example 1

[0024] Screening of oxidized lipid biomarker combination for early diagnosis of diabetic nephropathy

[0025] 1 Experimental Sample

[0026] All patients signed informed consent. The design principles and related experimental design plans of this project were approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (Ethics Review Number: 2022-KY-1408-001), which complies with the basic principles of the Declaration of Helsinki. The subjects were screened strictly according to the unified diagnostic criteria, inclusion criteria and exclusion criteria: a total of 60 healthy controls, 60 DKD patients aged 18 to 80 years, and 60 DM patients were included. The subjects in the DKD group were patients diagnosed with DKD by renal puncture biopsy in the Department of Nephrology of the First Affiliated Hospital of Zhengzhou University from December 2019 to June 2021. The DM group was DM patients who visited the Department of Endocrinology and Nephrology of this hospital during the same period. The subjects in the healthy control group were healthy volunteers selected from the physical examination center of the hospital during the same period. The age, gender and body mass index of the patients in each group were matched, and there was no significant difference. The subjects or patients in each group were randomly divided into training set samples and validation set samples.

[0027] Table 1 Number of samples in the training set and validation set

[0028] Healthy control group DKD group DM Group Training set 40 40 40 Validation set 20 20 20 Total number of samples 60 60 60

[0029] Diagnostic criteria for the DKD group were as follows: (1) aged between 18 and 80 years; (2) diagnosed with DKD in our hospital according to the clinical diagnostic criteria established by the ADA, and with a history of T2DM for more than 5 years; (3) not suffering from other clinically diagnosed serious diseases, including but not limited to tumors, neurological, digestive, mental disorders or infectious diseases.

[0030] Diagnostic criteria for DM patients: (1) aged between 18 and 80 years; (2) diagnosed with T2DM in our hospital for more than 5 years according to the clinical diagnostic criteria established by the American Diabetes Association (ADA); (3) no proteinuria and decreased estimated glomerular filtration rate (eGFR), and no signs of renal damage caused by other reasons; (4) no other clinically diagnosed serious diseases, including but not limited to tumors, neurological, digestive, mental disorders or infectious diseases.

[0031] Inclusion criteria for healthy controls: (1) aged between 18 and 80 years; (2) no self-reported or clinically diagnosed history of any chronic serious disease, including but not limited to tumors, neurological, kidney, digestive or psychiatric diseases, and infectious diseases within 3 months; (3) no underlying diseases such as hyperuricemia, metabolic syndrome, diabetes, hypertension, etc. that may damage the kidneys; (4) no treatment within 3 months before blood collection.

[0032] Exclusion criteria for patients in the DKD group were as follows: 1) previously diagnosed with type 1 diabetes, special types of diabetes, and gestational diabetes; (2) aged less than 18 years or greater than 80 years; (3) DKD not diagnosed by renal puncture in the past or during the current hospitalization; (4) combined with other kidney diseases (such as prerenal / renal / postrenal AKI, lupus nephritis, etc.); (5) a history of other chronic diseases such as tumors, hepatitis, tuberculosis, kidney disease, rheumatic diseases, etc.; (6) a history of surgery and acute infection in the past six months; (7) having received renal replacement therapy; (8) incomplete medical records.

[0033] Exclusion criteria for patients in the DM group were as follows: (1) younger than 18 years or older than 80 years; (2) positive urine protein in the past or after the current hospitalization; (3) a history of other chronic diseases such as tumors, hepatitis, tuberculosis, kidney disease, rheumatic diseases, etc.; (4) a history of surgery and acute infection in the past six months; and (5) incomplete medical records.

[0034] 2 Experimental methods

[0035] 2.1 Sample collection

[0036] All DKD, DM patients and healthy controls had their venous blood drawn on an empty stomach in the morning of the next day after enrollment. Disodium ethylenediaminetetraacetic acid was used for anticoagulation. Plasma was separated by centrifugation within 30 minutes after blood collection (centrifuge conditions: 4°C, 1500r / min, centrifugation for 10 minutes). The plasma was transferred to a clean 1.5ml EP tube and stored in a -80°C refrigerator for future use.

[0037] 2.2 Targeted analysis of peripheral plasma oxidized lipid levels using ultra-high performance liquid chromatography tandem mass spectrometry Detection instrument: Ultra-high performance liquid chromatography (UPLC) (ExionLC TM AD, https: / / sciex.com.cn / ) and tandem mass spectrometry (Tandem Mass Spectrometry, MS / MS) ( 6500+, https: / / sciex.com.cn / ).

[0038] Chromatographic conditions: chromatographic column: Waters ACQUITY UPLC HSS T3 C18 column (1.8 μm, 100 mm×2.1 mm i.d.); mobile phase: phase A, acetonitrile / water (60 / 40, V / V) (containing 0.002% acetic acid), phase B acetonitrile / isopropanol (50 / 50, V / V); flow rate 0.4 mL / min; column temperature 40°C; injection volume 10 μL. Mobile phase gradient: 0 min A / B is 99.9:0.1 (V / V), 2 min A / B is 70:30 (V / V), 4 min is 50:50 (V / V), 5.5 min is 1:99 (V / V), 7 min is 1:99 (V / V), 7.1 min is 99.9:0.1 (V / V).

[0039] Mass spectrometry conditions: Electrospray Ionization (ESI) temperature 550°C, mass spectrometry voltage -4500V, Curtain Gas (CUR) 35psi. In Q-Trap 6500+, each ion pair was scanned and detected according to the optimized Declustering Potential (DP) and Collision Energy (CE).

[0040] Sample processing: Take out the plasma sample from the -80°C refrigerator and thaw it on ice. The subsequent operations are carried out on ice. Take 100 μL of the liquid sample (vortex evenly), add 200 μL of methanol / acetonitrile (1:1, v / v) internal standard extraction solution. After vortexing for 5 min, store it in the -20°C refrigerator and let it stand for 30 min to precipitate proteins. Centrifuge at 12,000 r / min for 10 min at 4°C, and collect the supernatant. Re-extract once and combine the supernatants. Activate and equilibrate the solid-phase extraction column, load the sample, wash, elute, and collect the eluate. Concentrate the eluate to dryness, then re-dissolve it with 100 μL of methanol / water (1:1, v / v), vortex for 30 s, and take the supernatant for LC-MS / MS analysis.

[0041] 2.3 Data processing

[0042] Two independent samples T-test: Use IBM SPSS Statistics 22 software. First, use the F-test to determine whether the variances of the two populations are equal. If the variances are equal, observe the T-test probability value in the "Equal variances assumed" row of the analysis results; if the variances are not equal, observe the T-test probability value in the "Equal variances not assumed" row. Then, based on the observed value of the t statistic, observe the corresponding two-tailed probability P value. If 0.01 < P value < 0.05, it is considered that there is a statistical difference between the two populations; if P value < 0.01, it is considered that there is a significant difference between the two populations; if P value > 0.05, it is considered that there is no significant difference between the two populations.

[0043] 3 Results

[0044] Detect the levels of 141 oxidized lipids in the plasma of subjects in the DKD, T2DM, and healthy control (CON) groups according to the above UPLC-MS / MS method. The results are as Figure 1 shown. In the training set, there were significant differences in the contents of plasma arachidonic acid (ARA), plasma 5-hydroxyeicosatetraenoic acid (5-HETE), and plasma 5-oxoeicosatetraenoic acid (5-oxoETE) in the sera of healthy control population (CON), diabetes (DM) patients, and diabetic kidney disease (DKD) patients. Compared with the CON and DM groups, the levels of plasma ARA, plasma 5-HETE, and plasma 5-oxoETE in the DKD group were significantly increased (P < 0.01, P < 0.001).

[0045] Detect the plasma samples of the validation set. The results are as Figure 2As shown. In the validation set, the levels of plasma ARA, plasma 5-HETE and plasma 5-oxoETE in the DKD group were significantly higher than those in the CON and DM groups, with significant differences (P<0.01, P<0.001). This result is consistent with the test results of the plasma samples in the training set. The above can show that the levels of plasma ARA, plasma 5-HETE and plasma 5-oxoETE can be used as a biomarker combination to distinguish healthy people, diabetic people and diabetic nephropathy people.

[0046] Example 2

[0047] Diagnostic efficacy of plasma ARA, plasma 5-HETE and plasma 5-oxoETE for diabetic nephropathy

[0048] The receiver operating characteristic (ROC) curve was used to comprehensively evaluate the model effect and calculate the corresponding area under the ROC curve (AUC). First, the ROC curves were drawn for plasma ARA, plasma 5-HETE and plasma 5-oxoETE, and the correlation index was calculated. Then, a combined factor diagnostic model was constructed based on the above three oxidized lipid factors. Logistic regression was performed with the above three oxidized lipid factors to establish a combined factor regression equation. The regression equation was: Logit (P) = 0.001*Log(ARA) + 0.076*Log(5-HETE) + 0.570*Log(5-oxoETE) -11.895. The ROC curve was drawn and the correlation index was calculated.

[0049] The results are shown in Table 2 and Figure 3 As shown in the figure, the AUROC value of the diagnostic prediction model constructed with plasma ARA, plasma 5-HETE and plasma 5-oxoETE3 as joint factors was 0.965, which was significantly higher than the AUROC values ​​of the above three single factors of oxidized lipids. In addition, the Youden index, sensitivity and specificity of the above prediction model were significantly higher than those of the above three single factors of oxidized lipids. The cut-off value of the diagnostic prediction model constructed by the above joint factors for distinguishing healthy people from diabetic nephropathy patients was 0.626.

[0050] The above results show that the combined factor diagnostic prediction model provided by the present invention has a stronger predictive ability for the early diagnosis of diabetic nephropathy, and has better accuracy, sensitivity and specificity than the single factor diagnostic prediction models established using the above three oxidized lipids.

[0051] Table 2 Diagnostic efficacy of plasma ARA, plasma 5-HETE and plasma 5-oxoETE and their combination for diabetic nephropathy

[0052]

[0053] Note: Accuracy = (A × sensitivity + B × specificity) / (A + B). A is the total number of diabetic nephropathy in the sample set and validation set, and B is the total number of healthy controls and diabetics in the sample set and validation set.

[0054] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A biomarker composition based on oxidized lipids, characterized in that: The biomarker composition consists of plasma arachidonic acid ARA, plasma 5-hydroxyeicosatetraenoic acid 5-HETE, and plasma 5-oxoeicosatetraenoic acid 5-oxoETE.

2. Use of the oxidized lipid-based biomarker composition as claimed in claim 1 in the preparation of a test kit for early diagnosis of diabetic nephropathy.

3. A prediction model for early diagnosis of diabetic nephropathy, characterized in that: The prediction model uses plasma arachidonic acid ARA, plasma 5-hydroxyeicosatetraenoic acid 5-HETE, and plasma 5-oxoeicosatetraenoic acid 5-oxoETE as construction parameters, performs logistics regression, and establishes a joint factor regression equation; the regression equation is: Logit (P) = 0.001*Log (ARA) + 0.076*Log (5-HETE) + 0.570*Log (5-oxoETE) - 11.

895.

4. A prediction model for early diagnosis of diabetes according to claim 3, characterized in that: The cut-off value of the prediction model in diagnosing and distinguishing healthy people from patients with diabetic nephropathy is 0.626.

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