CN111289638A

CN111289638A - Application of serum metabolism marker in preparation of diabetic nephropathy early diagnosis reagent and kit

Info

Publication number: CN111289638A
Application number: CN202010076413.3A
Authority: CN
Inventors: 郑超; 毛广运; 张杭; 谷卫
Original assignee: Zhejiang University ZJU
Current assignee: Zhejiang University ZJU
Priority date: 2020-01-23
Filing date: 2020-01-23
Publication date: 2020-06-16

Abstract

The invention provides a novel diabetic nephropathy detection kit, which can be used for sensitively, accurately, simply and conveniently identifying early patients in a noninvasive manner compared with the traditional indexes and can be used as one of novel clinical auxiliary diagnosis means. The invention utilizes a UPLC-ESI-MS/MS (ultra performance liquid chromatography-electrospray ionization tandem mass spectrometry) method to screen out nine novel serum metabolic markers including hexadecanoic acid, elaidic acid, azoxystrobin acid, lysophosphatidylcholine (20:4), piperidine, 6-methylmercaptopurine, 6-aminocaproic acid, L-hydrogenated orotic acid and cuminaldehyde related to early diabetic nephropathy. The early research data show that the group of metabolites can be used for diagnosing patients with and without early diabetic nephropathy through single or combined detection, so that the basis is provided for early intervention and treatment of clinicians, and the occurrence of end-stage nephropathy is reduced to the maximum extent.

Description

Application of serum metabolism marker in preparation of diabetic nephropathy early diagnosis reagent and kit

Technical Field

The invention relates to an application of serum metabolite as a specific biomarker in early diagnosis of diabetic nephropathy.

Technical Field

Diabetic nephropathy (DKD), formerly known as Diabetic Nephropathy (DN), is a common microvascular complication in patients with Diabetes Mellitus (DM). The damage to the kidney caused by the damage not only affects blood vessels, glomeruli and renal tubules, but also affects almost all kidney structures such as renal interstitial parts and podocytes.

Microalbuminuria (MA) and estimated glomerular filtration rate (eGFR) are currently the more accepted indicators for the clinical diagnosis of early DKD. However, clinically once microalbuminuria is detected and substantial damage to the kidney has occurred, there will be a progressive irreversible decline in glomerular function; and clinically, a part of DM patients with urinary albumin in a normal range have actually suffered from glomerular filtration rate reduction, and characteristic changes related to DKD can be found in renal pathology; the clinically available treatments can only slow the progression of DKD to renal failure, and are far from reversing its course. Therefore, there is a need to increase the level of detection of impaired renal function in the absence of symptoms, to look for additional biomarkers to provide DKD lesions that are more early and more accurately reflected in clinical silent periods, and to better predict the risk of DKD progression. The method has great significance for early risk assessment of DKD and detection and prognosis of disease progression.

Metabolomics, refers to the systematic and comprehensive analysis of metabolites (i.e., sugars, amino acids, organic acids, nucleotides, bile acids, acyl carnitines, lipids, etc.). In biological samples, it is a powerful tool for finding biomarkers. Currently, methods for metabonomics study are Nuclear Magnetic Resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and capillary electrophoresis mass spectrometry (CE-MS). Metabolomics approaches hold great promise in nephrology research, as renal function may have a broad impact on the functional role that circulating metabolite levels may play in DKD pathogenesis and its complications. Significant changes in metabolites in the kidney have been discovered and these findings make them a valuable adjunct tool in understanding, diagnosing and aiding in the treatment of kidney disease.

Disclosure of Invention

The purpose of the present invention is to provide the use of serum metabolites for the early specific clinical diagnosis of DKD.

In order to solve the above problems, the present invention provides the following technical solutions:

use of metabolites in serum for early specific clinical diagnosis of DKD. Serum metabolic markers: hexadecanoic acid, elaidic acid, 6-aminocaproic acid, L-hydrogenated orotic acid, 6-methylmercaptopurine, piperidine, azoxystrobin acid, lysophosphatidylcholine (20:4), cuminaldehyde.

The DKD was diagnosed using UPLC-ESI-MS/MS to detect metabolites in serum.

The application of the serum metabolite to the preparation of a DKD early diagnosis reagent or kit.

Further, the diagnostic kit is a standard comprising a single or a plurality of combined test metabolic markers, the standard being in a solid form or a formulated solution.

Further, the diagnostic kit is a single standard of hexadecanoic acid, elaidic acid, 6-aminocaproic acid, L-hydrogenated orotic acid, 6-methylmercaptopurine, piperidine, azoxystrobin acid, lysophosphatidylcholine (20:4), cuminaldehyde.

Further, the diagnostic kit is a combination of standards of linoleic acid, L-hydrogenated orotic acid and azoxystrobin acid.

The use method of the diagnostic kit comprises the following steps: taking a serum sample of a subject, adopting a diagnostic kit as an internal standard, and analyzing through UPLC-ESI-MS/MS to quantify the concentration of the metabolic marker in the serum sample of the subject.

The invention provides a new diagnosis means for clinical diagnosis of DKD and has certain potential clinical value in assisting the treatment and disease course monitoring of DKD. Clinical experiments prove that the change of individual serum metabolites has higher sensitivity and specificity for DKD diagnosis. The comprehensive accuracy of the combined detection of the metabolites is proved to be higher, and the misdiagnosis rate is obviously reduced.

Drawings

FIG. 1 is a graph of the orthogonal partial least squares discriminant analysis (OPLS-DA) scores for the DKD group versus the non-DKD group, wherein the triangles represent the DKD group and the circles represent the non-DKD group, it can be seen that two groups of patients can be completely distinguished by the metabolic molecules of the present invention by principal component analysis;

FIG. 2 shows the results of 10 cross validation tests and 1000 permutation tests on two groups of patients, and the results of 10 cross validation tests and 1000 permutation tests on two groups of patients show that the model has higher stability, and the batch of substances are verified to be metabolic molecules with DKD specifically and stably changing again

FIG. 3 is a ROC graph of serum metabolic marker diagnosis of DKD, which shows the diagnostic value of each serum metabolic marker and combination of elaidic acid, L-hydrogenated orotic acid and azoxystrobin acid on DKD, and shows that all serum metabolic markers involved in the present invention have ROC-AUC of early DKD over 0.7, and three molecules of elaidic acid, L-hydrogenated orotic acid and azoxystrobin acid have AUC-ROC of early DKD up to 0.93 (95% CI: 0.85-1) when combined.

Detailed Description

The invention will be further illustrated with reference to specific embodiments. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Furthermore, it should be understood that various changes and modifications can be made by those skilled in the art after reading the disclosure of the present invention, and equivalents fall within the scope of the appended claims.

The invention selects 44 hospitalized patients with type 2 diabetes mellitus in the previous case control study, and the patients are divided into a pure DM group (n is 20, the diagnosed T2DM patients do not contain any diabetic complication), an early DKD group (n is 24, the early DKD stage is confirmed, the age, the sex and the course are completely matched with the control group), after the blood sugar and blood pressure control is stable, fasting serum is reserved, UPLC-ESI-MS/MS is utilized to respectively detect the expression change of metabolites of each sample, the relation between the relative expression quantity and clinical indexes is analyzed, whether the metabolites can be used as specific biomarkers for the DKD early diagnosis is verified, the correlation of serum metabolites of the same individual and the diagnosis specificity and sensitivity of the serum metabolites are analyzed, the sensitivity and the specificity of the ratio (UACR) of the serum metabolites to the urinary albumin/creatinine in the renal function injury diagnosis are compared, the diagnosis efficiency of the single detection and the combined detection of the metabolites is compared, and the reference value of the DKD protein as DKD detection and prognosis is explored.

1. Protocol and sample Collection

Biochemical indexes such as urine trace protein, urine creatinine, fasting plasma glucose, glycated hemoglobin, blood cholesterol, triglyceride, high density lipoprotein, low density lipoprotein, vitamin D, and the like were measured in 20 pure diabetic patients and 24 DKD patients, respectively (table 1). Two groups of patients were bled 6ml after fasting for 8 hours and centrifuged at 2000rpm for 10 minutes over 3 hours to separate the serum. All serum samples were frozen at-80 ℃ prior to sampling.

2. Sample processing

All samples were thawed on ice to extract metabolites. 50. mu.L of serum was added to 150. mu.L of ice-cold methanol, and after stirring well, the mixture was centrifuged at 12,000rpm at 4 ℃ for 10 minutes. Subsequently, the supernatant was collected and centrifuged at 12,000rpm for 5 minutes at 4 ℃. Finally, the supernatant was extracted for UPLC-MS/MS analysis.

UPLC-ESI-MS/MS analysis

Ultra high performance liquid chromatography (UPLC, Shim-pack UFLC Shimadzu CBM30A system), https:// www.shimadzu.com /) and tandem mass spectrometry (

6500+ system, https:// sciex. com /),) was analyzed using an LC-ESI-MS/MS system. The analysis conditions were: UPLC column, Waters CQIITY UPLC HSS T3C 18(1.8 μm, 2.1 mm. times.100 mm), column temperature 40 ℃, flow rate 0.4mL/min, sample size 2 μ L, solvent system: water (0.04% acetic acid): acetonitrile (0.04% acetic acid), flow rate 0.4mL/min, sample size 2 mol/L, solvent system: water (0.04% acetic acid): acetonitrile (0.04% acetic acid); gradient program, 0 min 95: 5V/V, 11.0 min 5: 95V/V, 12.0 min 5: 95V/V, 12.1 minA clock 95: 5V/V, 14.0 min 95: 5V/V. A triple quadrupole linear ion TRAP mass spectrometer (Q TRAP) was used,

6500+ LC-MS/MS system equipped with ESI Turbo ion spray interface, under the control of Analyst 1.6.3 software (AB Sciex), working in positive and negative ion mode, and obtaining LIT and triple quadrupole (QQQ) scan. The source operating parameters include: source temperature 500 ℃, ion spray voltage (IS) 5500V (positive), -4500V (negative), ion source GAS i (GSI), GAS II (GSI II), curtain GAS (CUR) 55, 60 and 250 psi, respectively, and collision GAS (CAD) height. Instrument tuning and mass calibration were performed in QQQ and LIT modes with 10 and 100mol/L polypropylene glycol solutions. Each individual period follows a specific set of MRM transitions based on the metabolites that elute during that period.

Data processing and analysis

After serum metabolite evaluation, 158 was used

Software 1.6.3(AB SCHIEX) UPLC-ESI-MS/MS data were collected using MultiQuant Analyst4.0(https:// www.metaboanalyst.ca /)25 and Stata MP15.0(Stata 161^TMUniversity of Texas, USA) pre-processing (conversion, peak detection, retention 159 time correction and peak alignment) and 160 metabanalyze 4.0(sta 161Corp, College Station, USA, Texas) process the data. Normalization using summation and pareto scaling (centered on the mean divided by the square root of the standard deviation of each variable) normalized the data to make the features more comparable. Differential metabolites were detected in 24 patients and 20 control groups using Principal Component Analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), Fold Change (FC) analysis, and t-test, respectively. In addition, 10 cross-validation tests and 1000 alignment tests were also performed. The multiple hypothesis testing and reduction of false positives were adjusted using an additive false rate (FDR) method. The criteria for differential metabolite determination were: q value (FDR adjusted p value) < 0.05, FC value (case/control ratio) > 1.2 or < 0.8, variable importance in project (VIP) > 1. Subsequently, multivariate generalized linear regression model (GLM) was applied to analyze the relationship of each differential metabolite to the incidence of DKD. In addition, the value of metabolic markers in the early diagnosis of DKD was assessed by Receiver Operating Characteristic (ROC) analysis. All statistical tests were bilateral tests, with p ≦ 0.05 treated as the level of significance. And (3) carrying out statistical analysis on the relative content of each compound in each group (a control group and DKD early stage), determining the compounds with significant difference between a normal group and the DKD early stage, and selecting the compounds with the largest statistical difference and the smallest relative error among 2 groups for diagnosing the metabolic markers of the DKD early stage.

Results

Comparison of general clinical data of each group

Two groups of 44 patients were analyzed for serum metabolic profiles. The DKD group had lower high density lipoprotein cholesterol (HDL-C) than the non-DKD group. Other clinical indicators the difference between the two groups was not significant (P > 0.05) (see Table 1).

TABLE 1 clinical characterization

Differential metabolites between two groups

Complete separation of 24 DKD patients and 20T 2DM patients was observed in OPLS-DA scoring scatter plots, suggesting that serum metabolites may effectively distinguish DKD patients from T2DM patients. The OPLS-DA model involves one predicted component and one orthogonal component (R _2X _ (Cum) ═ 70%, R _2Y _ (Cum) ═ 83%, Q _ (Cum2) ═ 56%), indicating that the model has high stability (see fig. 1). In addition, the OPLS-DA model was further validated by alignment verification (see FIG. 2).

Comparison of metabolite expression levels between two groups

Compared with a diabetes control group, the UPLC-ESI-MS/MS measurement shows that the content of hexadecanoic acid, elaidic acid, azoxystrobin acid and lysophosphatidylcholine (20:4) in early DKD serum is obviously lower than that in the control group; 6-aminocaproic acid, L-hydrogenated orotic acid, 6-methylmercaptopurine, piperidine, cuminaldehyde were higher than the control (Table 2).

TABLE 2 comparison of expression levels of two groups of metabolic markers

Correlation analysis of metabolic markers with clinical indices

Spearman correlation analysis of the metabolic markers with the urine albumin/creatinine ratio (UACR) showed that UACR was positively correlated with 6-aminocaproic acid, L-hydrogenated orotic acid, 6-methylmercaptopurine and cuminaldehyde, and with hexadecanoic acid, elaidic acid, piperidine and azoxystrobin acid.

TABLE 3 Spearman correlation analysis of metabolites with UACR

Thus, hexadecanoic acid, elaidic acid, azoxystrobin acid, lysophosphatidylcholine (20:4), piperidine, 6-methylmercaptopurine, 6-aminocaproic acid, L-hydrogenated orotic acid, cuminaldehyde can be used as markers for early specific clinical diagnosis of DKD.

Diagnostic value of serum metabolic markers for early stage DKD patients

In clinical work, the actual situation of a patient is far more complex than theory, and because MA and eGFR are possibly interfered by various factors, sensitivity and specificity are not ideal, the method applies specific serum metabolic molecules to the first line of clinic as diagnosis indexes and means, is a new attempt, and simultaneously, in order to clarify the sensitivity and specificity of the method, the diagnosis values of various metabolic molecules are further discussed.

Respectively drawing ROC curves of the serum metabolic markers by taking the ordinate as sensitivity and the abscissa as false positive rate (1-specificity), judging the diagnostic effect of the serum metabolic markers according to the area under the ROC curve (ROC-AUC), and generally indicating that the diagnostic value is lower when the AUC is less than or equal to 0.7; the diagnostic value is medium when the AUC is 0.7< 0.85, and the diagnostic value is high when the AUC is greater than 0.85. When the three molecules of the linoleic acid, the L-hydrogenated orotic acid and the azoxystrobin acid are combined, the AUC-ROC of early DKD is up to 0.93 (95% CI: 0.85-1); AUC-ROC of early DKD also reached 0.85 (95% CI: 0.72-0.97) as diagnosed by a single molecule such as L-hydrogenated orotic acid; all of the serum metabolic markers involved in the present invention have a ROC-AUC of over 0.7 in the diagnosis of early DKD. (see Table 4, FIG. 3)

TABLE 4 AUC values for DKD diagnosis for each serum metabolic marker

Metabolic markers	AUC	95％CI	SE
				Palmitic acid	0.8	0.67,0.93	0.07
Translinoleic acid	0.82	0.69,0.95	0.06
				6-aminocaproic acid	0.76	0.61,0.91	0.07
L-hydrogenated orotic acid	0.85	0.72,0.97	0.06
				6-Methylmercaptopurine	0.71	0.55,0.86	0.08
Piperidine derivatives	0.73	0.58,0.88	0.08
				Azoxystrobin acid	0.83	0.70,0.96	0.07
Lysophosphatidylcholine (20:4)	0.71	0.56,0.87	0.08
				Cuminaldehyde	0.72	0.57,0.87	0.07
Combinations (elaidic acid, L-hydrogenated orotic acid, azoxystrobin acid)	0.93	0.85,1.00	0.04

The above results indicate that the preferred marker combination is L-hydrogenated orotic acid or elaidic acid, L-hydrogenated orotic acid and azoxystrobin acid, and that the preferred single markers are palmitic acid, elaidic acid, L-hydrogenated orotic acid and azoxystrobin acid.

Kit preparation content and result judgment standard

The invention provides an early diagnosis marker of DKD and application of a combination, which can be used for preparing an early DKD diagnosis kit. The diagnostic kit comprises all standard products of the diagnostic markers or stable isotope labeled compounds which are the same as the diagnostic markers to be detected, and the stable isotope labeled compounds are used for quantifying and correcting the diagnostic markers to be detected. A preferred diagnostic sample of a diagnostic marker is serum.

When DKD diagnosis is judged, the relative content of each diagnosis marker can be obtained from the measurement result of a control group sample to be used as a normal range, and when the relative content exceeds the normal range and is consistent with the direction caused by diseases, early DKD diagnosis can be preliminarily planned. The result obtained by the combined detection is more reliable than the single detection result, and the ratio of the change of the metabolic molecule expression quantity compared with the control group can also be used as one of the judgment standards. Alternatively, molecules with an inverse trend of relative serum content in patients with early DKD can be selected, and the ratio calculated and compared to that of control population for diagnosis.

The diagnostic kit may comprise a mixed standard of all the biomarkers tested or a single standard of all the biomarkers to be tested. The standard may be a solid or a formulated solution. If the standard is in solid form, it needs to be dissolved in a suitable formulation solution prior to testing. The diagnostic kit comprises at least one of the substances listed in table 2.

Diagnostic kits typically include at least one container for holding a standard. This container may be single-compartment or multi-compartment. In some diagnostic kits, the container is also suitable for use in the measurement of biomarkers, and can be used for standard detection or instrumental analysis, such as chromatography-mass spectrometry, of biomarkers in a sample to be tested.

In some diagnostic kits, the biomarker standard to be tested must be distributed in one or more wells.

Diagnostic kit containers are generally designed for the determination of liquid samples, such as biological fluids or extracts of solutions which are biosolid samples but have been processed.

Single and combination standards of biomarkers were tested, either in solid form or as a formulated solution.

The diagnostic kit also includes a solvent for standard preparation or sample testing. These solvents were stored in different containers. The solvent includes but is not limited to one or more of water, methanol, acetonitrile and acetic acid.

The diagnostic kit also needs a microporous plate suitable for liquid chromatography-mass spectrometry or gas chromatography-mass spectrometry. The microplate must contain sufficient wells to accommodate at least one standard and one sample to be tested. These standards are of known concentration and are configured such that a series of standard solutions of known concentration are placed in different microwells. After the standard solution is placed in the microporous plate, the sample to be detected is also placed on the microporous plate.

The diagnostic kit can diagnose a risk of DKD in a subject using the quantitative levels of diagnostic markers provided by the invention.

The kits of the invention can also be used to evaluate a biological sample derived from a DKD individual or a suspected DKD individual. In some kit applications, the diagnostic biological sample may not require pre-treatment or may require prior pre-treatment.

Samples can be obtained from subjects clinically and transported to a laboratory for detection and evaluation using the kits of the invention. Or samples taken at a medical facility where the test can be performed and subsequently tested and evaluated using the kit of the invention.

The kit comprises the following specific operation steps:

1. obtaining a serum sample of a test subject;

2. determining a serum sample of a subject by chromatography coupled with mass spectrometry metabolomics analysis, wherein said chromatography comprises liquid chromatography and gas chromatography; the mass spectrum comprises flight time, ion hydrazine, a quadrupole rod, a magnetic good region, ion cyclotron resonance, a static electric fan or any combination of the flight time, the ion hydrazine, the quadrupole rod and the magnetic good region;

3. comparing the result with a standard substance to evaluate the DKD risk; one of the criteria for evaluation was determined by taking the value of the control group (internal standard correction) as a reference control, setting the mean value to 1, calculating the relative value and 95% confidence interval of the DKD group (Table 5) as a criterion for evaluating the diagnosis of DKD

TABLE 5 in vivo relative amounts of DKD patient metabolic markers and confidence intervals

In a specific embodiment, the level of the diagnostic marker is determined using UPLC-ESI-MS/MS.

The above description is only a preferred embodiment of the present invention, and it should be noted that, for a person skilled in the art, several optimization improvements and additions can be made without departing from the method of the present invention, and these improvements and additions should also be considered as the protection scope of the present invention.

Claims

1. Serum metabolic markers associated with early diagnosis of diabetic nephropathy: hexadecanoic acid, elaidic acid, 6-aminocaproic acid, L-hydrogenated orotic acid, 6-methylmercaptopurine, piperidine, azoxystrobin acid, lysophosphatidylcholine (20:4), cuminaldehyde.

2. The use of the serum metabolic markers in the preparation of the kit for the early diagnosis of diabetic nephropathy according to claim 1, wherein the diagnosis kit is a standard comprising single or multiple combined test metabolic markers, and the standard is in a solid form or a prepared solution.

3. Use according to claim 2, characterized in that the diagnostic kit is a single standard of hexadecanoic acid, elaidic acid, 6-aminocaproic acid, L-hydrogenated orotic acid, 6-methylmercaptopurine, piperidine, azoxystrobin acid, lysophosphatidylcholine (20:4), cuminaldehyde.

4. Use according to claim 2, wherein the diagnostic kit is a combination of standards for linoleic acid, L-hydroorotic acid and azoxystrobin acid.

5. The use according to claim 2, wherein the diagnostic kit is used in a method comprising the steps of: taking a serum sample of a subject, adopting a diagnostic kit as an internal standard, and analyzing through UPLC-ESI-MS/MS to quantify the concentration of the metabolic marker in the serum sample of the subject.

6. Use of the metabolite according to claim 1 in the preparation of an early diagnostic reagent for diabetic nephropathy.