Application of a biomarker in the preparation of reagents for early diagnosis of nephrotic syndrome

Abstract

The present invention provides an application of a biomarker in the preparation of an early diagnosis reagent for nephrotic syndrome, wherein the biomarker is galactitol, sorbitol, mannitol, nicotinamide adenine dinucleotide, ketoglutaric acid analogs, bilirubin, and reduced nicotinamide adenine dinucleotide phosphate. The present invention verifies that the biomarker is closely related to the occurrence and development of hormone-sensitive nephrotic syndrome in children. By checking the expression of the marker in the blood, it can be applied to the early diagnosis of nephrotic syndrome, with high accuracy, sensitivity, and specificity, providing an important scientific basis for the early diagnosis of hormone-sensitive nephrotic syndrome in children and monitoring the occurrence and development of the disease, and providing valuable biological information for the study of hormone-sensitive nephrotic syndrome in children.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology and relates to the application of a biomarker in the preparation of a kit for early diagnosis of nephrotic syndrome. The present invention adopts non-targeted metabolomics and targeted metabolomics mass spectrometry detection methods to screen and identify the early diagnosis and occurrence and development of hormone-sensitive nephrotic syndrome in children. Technical Background

[0002] Nephrotic syndrome (NS) is a clinical syndrome caused by a series of pathological and physiological changes due to the increased permeability of the glomerular filtration membrane to plasma proteins and the loss of a large amount of plasma proteins in the urine. The clinical characteristics are massive proteinuria, hypoproteinemia, hyperlipidemia and edema. Nephrotic syndrome can be divided into three types: primary, secondary and congenital. Primary INS (Idiopathic Nephrotic Syndrome) accounts for about 90% of the total number of NS in children (Filler G, Young E, Geier P, Carpenter B, Drukker A, Feber J. Is there really an increase in non-minimal change nephrotic syndrome in children? Am J Kidney Dis. 2003 Dec; 42 (6): 1107-13. doi: 10.1053 / j.ajkd.2003.08.010. PMID: 14655180.), which is one of the common glomerular diseases in children. Foreign reports show that the annual incidence of INS in children is (2-4) / 100,000, and the prevalence is 16 / 100,000 (Eddy AA, Symons JM. Nephrotic syndrome in childhood. Lancet. 2003Aug 23; 362(9384):629-39. doi:10.1016 / S0140-6736(03)14184-0. PMID:12944064; Wong W. Idiopathic nephrotic syndrome in New Zealand children, demographic, clinical features, initial management and outcome after twelve-month follow-up: results of a three-yearnational surveillance study. J Paediatr Child Health.2007May;43(5):337-41.doi:10.1111 / j.1440-1754.2007.01077.x.PMID:17489822.), statistics from 37 cooperative hospitals in my country showed that PNS accounted for about 20% of the total number of children hospitalized with urinary system diseases during the same period.(National Survey Working Group on the Current Status of Diagnosis and Treatment of Common Kidney Diseases in Children. A multicenter study on the current status of diagnosis and treatment of hormone-sensitive, relapsing / dependent nephrotic syndrome in children in my country [J]. Chinese Journal of Pediatrics, 2014, 52(3): 194-200. DOI: 10.3760 / cma.j.issn.0578-1310.2014.03.007.) This disease seriously harms the physical and mental health of children and imposes a huge economic burden on the families of children and society. Glucocorticoid (GC) is a globally recognized first-line treatment for nephrotic syndrome. According to the initial response to hormone therapy, nephrotic syndrome can be divided into hormone-sensitive nephrotic syndrome (SSNS; defined as patients with negative urine protein after ≤4 weeks of initial treatment with adequate hormones) and hormone-resistant nephrotic syndrome (SRNS; defined as patients with positive urine protein after >4 weeks of initial treatment with adequate hormones). 80%-90% of pediatric patients have hormone-sensitive nephrotic syndrome (Zotta F, Vivarelli M, Emma F. Update on the treatment of steroid-sensitive nephrotic syndrome. Pediatr Nephrol. 2022Feb;37(2):303-314.doi:10.1007 / s00467-021-04983-3.Epub 2021Mar 5.PMID:33665752.). The pathogenesis of this type of nephrotic syndrome is still unclear and complex and diverse. Genetic factors, immune disorders, inflammatory mediators, hyperlipidemia, massive proteinuria and other metabolic factors may all be involved, delaying early identification and early detection. At present, according to the 2021 KDIGO guidelines and the 2021 Chinese Medical Association Pediatric Branch Nephrology Group Guidelines, the early diagnosis of INS mainly relies on genetic testing and renal biopsy, while genetic testing is time-consuming and expensive, and renal biopsy is an invasive test. Therefore, a new non-invasive and immediate diagnostic method is urgently needed to facilitate clinicians to identify children with INS more quickly and accurately.

[0003] With the completion of human genome sequencing, the study of gene function has gradually become a hot topic, and a series of "omics" studies have emerged, including transcriptomics, which studies the transcription process, proteomics, which studies all proteins and their functions in a biological system, and metabolomics, which studies the changes in metabolites and metabolic pathways. Different from other omics, metabolomics is the science of studying the changes in all metabolites produced by external stimuli in biological systems (cells, tissues or organisms). Its research objects are small molecule compounds with a relative molecular weight of less than 1500 (including sugars, amino acids, organic acids, nucleic acids, lipids, etc.), and small molecule compounds are identified and quantified. It uses modern high-throughput and high-sensitivity detection methods to systematically analyze metabolites in biological samples. The core concept is that the body's metabolic state reflects the body's overall condition and is also affected by factors such as genomic coding, diet, and environment (Beger RD, Dunn W, Schmidt MA, Gross SS, Kirwan JA, Cascante M, Brennan L, Wishart DS, Oresic M, Hankemeier T, Broadhurst DI, Lane AN, Suhre K, Kastenmüller G, Sumner SJ, Thiele I, Fiehn O, Kaddurah-Daouk R; for "Precision Medicine and Pharmacopeia Metabolomics Task Group"-Metabolomics Society Initiative. Metabolomics sense precision medicine: "A White Paper, Community Perspective". Metabolomics. 2016; 12(10): 149. doi: 10.1007 / s11306-016-1094-6. Epub 2016Sep2. PMID: 27642271; PMCID: PMC5009152.). Kidney is the main organ for metabolism and elimination of small molecule compounds in the human body. Impaired renal function can lead to changes in serum or urine metabolite concentrations and can serve as a predictor of kidney disease. Therefore, metabolomics is a very prospective and practical method to study kidney disease.At present, there are reports on the application of metabolomics to find metabolic markers of kidney diseases such as acute kidney injury, chronic kidney disease, autosomal dominant polycystic kidney disease and renal cancer (Fanos V, Fanni C, Ottonello G, Noto A, Dessì A, Mussap M. Metabolomics in adult and pediatric nephrology. Molecules. 2013Apr 24; 18(5): 4844-57. doi: 10.3390 / molecules18054844. PMID: 23615531; PMCID: PMC6270081.). So far, the traditional urine protein and blood albumin levels are still used as indicators for clinical diagnosis and monitoring of the occurrence and development of hormone-sensitive nephrotic syndrome in children, which is lagging behind. There is still a lack of early clinical specific markers and reliable markers reflecting the occurrence and development of the disease. Therefore, it is very important to discover biomarkers for the early diagnosis of hormone-sensitive nephrotic syndrome in children and monitor them in time. Summary of the invention

[0004] The purpose of the present invention is to provide an application of a biomarker in the preparation of a kit for early diagnosis of nephrotic syndrome (SSNS), wherein the biomarker includes Dulcitol (galactitol), D-Sorbitol (sorbitol), D-Mannitol (mannitol), NAD (nicotinamide adenine dinucleotide), α-KG like (ketoglutaric acid analog), Bilirubin (bilirubin), and NADPH (reduced nicotinamide adenine dinucleotide phosphate). The present invention can predict whether a patient has abnormal renal function according to the expression amount of the marker in the blood, thereby performing early diagnosis of nephrotic syndrome.

[0005] The present invention verifies and finds that the biomarkers are closely related to the occurrence and development of SSNS, and their application in early diagnosis has good diagnostic efficacy, high accuracy, sensitivity and specificity, and provides valuable biological information for early diagnosis of SSNS and monitoring the occurrence and development of the disease.

[0006] The present invention firstly detected the non-targeted metabolomics of serum and urine of patients with initial SSNS in the discovery set (56 in the control group and 27 in the INS patients) by UHPLC-QTOF-MS method. The results showed that compared with urine, the difference between the serum of SSNS patients and the healthy group was more obvious. And after one week of medication, even if the patient's urine protein turned negative, there was still a significant difference from the healthy group, which also suggested that metabolites can better reflect the real physiological changes of the disease than traditional urine protein indications, and can also better explain the intrinsic factors of relapse in patients with nephrotic syndrome. The results of differential pathway enrichment analysis showed that serum metabolites of SSNS patients had abnormal amino acid metabolism. Further combined with the causal prediction analysis model, we found that 8 molecules were closely related to the occurrence of SSNS, among which Dulcitol, D-Sorbitol, D-Mannitol, Xanthosine 5'-monophosphate (XMP) were positively correlated, and NADPH, NAD, Bilirubin, alpha-Ketoglutaric acid (α-KG) were negatively correlated. The detection method of these 8 metabolites was established by triple quadrupole targeted mass spectrometry detection, and verified in the validation set (65 people in the control group and 65 people in INS patients). The results showed that 6 of the molecules (Dulcitol, D-Sorbitol, D-Mannitol, NADPH, NAD, Bilirubin, etc.) were consistent with the trend obtained by non-targeted metabolomics screening. Among them, there was a significantly different molecule that was extremely similar to the α-KG peak position. However, after multiple verifications, although it also appeared under the α-KG mass spectrometry elution conditions, it was obviously a different substance. Therefore, we describe it as an α-KG like substance, which also has obvious differences.

[0007] Dulcitol: Dulcitol, not much research has been done so far, with only one report suggesting that it is significantly decreased in the urine of patients with renal cancer. D-Sorbitol: Sorbitol, a diuretic, has been reported to be upregulated in the serum of patients with uremic syndrome. D-Mannitol: Mannitol, its effects on the kidneys are well understood, and its ability to induce nephrotoxicity has also been reported. NAD: Nicotinamide adenine dinucleotide, an important coenzyme that regulates multiple metabolic pathways, is significantly decreased in patients with AKI. Bilirubin: Bilirubin, the major end product of heme breakdown, is an important component of bile and is responsible for its characteristic color. Bilirubin itself should be considered as a potential cause of acute tubular necrosis. NADPH: Reduced nicotinamide adenine dinucleotide phosphate, can induce chemiluminescence and lipid peroxidation in renal microsomes.

[0008] The innovative points of the present invention are summarized as follows:

[0009] (1) Compared with other previous studies, the present invention conducted metabolomics analysis on serum and urine samples of patients with newly diagnosed SSNS and confirmed that metabolites in serum were more differentiated.

[0010] (2) Compared with other previous studies, the present invention performed metabolomics analysis on urine and blood samples of SSNS children who used adequate hormones for 0 weeks and 1 week, respectively, indicating that hormone treatment had no significant effect on metabolites after one week.

[0011] (3) Compared with other previous studies, the present invention not only performed non-targeted metabolomics detection, but also carried out targeted verification of related markers and optimized the marker detection method, which can detect a large number of samples at the same time and shorten the detection time. The confirmed differential metabolites have high accuracy, sensitivity and specificity, providing an important basis for the early diagnosis and monitoring of the occurrence and development of hormone-sensitive nephrotic syndrome in children, filling the gap at home and abroad.

[0012] (4) The biomarkers of the present invention are related to the early diagnosis and development of hormone-sensitive nephrotic syndrome (SSNS) in children and can be used in early diagnosis kits for SSNS. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 : PCA and OPLS-DA analysis of non-targeted metabolites in serum; where: (A) Principal component analysis (PCA) of non-targeted metabolite profiles detected by UHPLC-QTOF-MS in serum samples in positive and (B) negative ion modes. PCA analysis shows significant metabolite separation in healthy subjects and SSNS groups. (C) Orthogonal partial least squares discriminant analysis (OPLS-DA) score plots of metabolites in serum samples in positive and (D) negative ion modes.

[0014] Figure 2 : PCA and OPLS-DA analysis of non-targeted metabolites in urine, including: (A) PCA score plots of non-targeted metabolite profiles detected by UHPLC-QTOF-MS in serum samples in positive and (B) negative ion modes. PCA analysis plots showed no obvious metabolite separation between healthy subjects and SSNS groups. (C) OPLS-DA analysis plots of metabolites in urine samples in positive and (D) negative ion modes.

[0015] Figure 3 :Global pathway analysis was performed using MetaboAnalyst in positive ion mode. The figure lists the metabolites that were differentially detected in the SSNS group under positive ion mode, as well as the metabolic pathways in which these metabolites were enriched. Pathways for glycine, serine, and threonine metabolism, alanine, aspartate, and glutamate metabolism, taurine and hypotaurine metabolism, phenylalanine metabolism, and tryptophan metabolism.

[0016] Figure 4 :Global pathway analysis was performed using MetaboAnalyst in negative ion mode. The figure lists the metabolites that were differentially detected in the SSNS group under negative ion mode, as well as the metabolic pathways in which these metabolites were enriched. Pathways for alanine, aspartate, and glutamate metabolism, glycine, serine, and threonine metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, histidine metabolism, and arginine biosynthesis.

[0017] Figure 5 : Volcano plot of metabolites detected based on non-targeted analysis in positive ion mode, including: (A) Volcano plot of metabolites detected based on non-targeted analysis in negative ion mode; (B) Disease pathways enriched for differential metabolites in positive ion mode; (C) Disease pathways enriched for differential metabolites in negative ion mode; (D) Disease pathways enriched for differential metabolites in positive ion mode; (E) ① purine metabolism, ② galactose metabolism, ③ valine, leucine and isoleucine biosynthesis, ④ fructose and mannose metabolism, ⑤ glycine, serine and threonine metabolism, ⑥ aminoacyl-tRNA biosynthesis; (F) Pathways of differentiated metabolites in negative ion mode: ① alanine, aspartate and glutamate metabolism, ② arginine biosynthesis, ③ aminoacyl-tRNA biosynthesis, ④ citric acid cycle (TCA cycle), ⑤ histidine metabolism, ⑥ D-glutamine and D-glutamate metabolism.

[0018] Figure 6 : Expression profiles of eight potential metabolic markers associated with SSNS.

[0019] Figure 7 : Causal effect estimation of eight metabolites in the SSNS group.

[0020] Figure 8 : Targeted quantitative detection of eight metabolites in the SSNS group. DETAILED DESCRIPTION

[0021] The present invention is further described in conjunction with the accompanying drawings and embodiments.

[0022] Reagents and instruments used in the embodiments of the present invention:

[0023]

[0024] Example 1 Marker Screening

[0025] Step 1. Study population and sample collection, sample pretreatment

[0026] This study was approved by the ethics committee (ethics number: 2020-IRBAL-092) and was conducted in accordance with the Declaration of Helsinki. The study included subjects aged 6 to 276 months who were registered in the hospital since 2020. Serum samples from 27 SSNS patients and 56 healthy children were collected as controls. Regarding urine samples, samples were collected from 17 children with SSNS and 27 healthy children. SSNS can be diagnosed when the 24-hour urine protein excretion is ≥50 mg / kg, the morning urine protein / creatinine is >2 mg, hypoalbuminemia is <25 g / L, and the urine protein anion is ≤4 weeks in patients treated with adequate prednisone [2 mg / (kg-d) or 60 mg / (md)]. Unconditional logistic regression was performed with corresponding CI (95%), and statistics were performed for age, body mass index (BMI), urine microalbumin (UAlb), urine creatinine (UCr), urine protein-creatinine ratio (Up / Ucr), serum creatinine (sCr), triglycerides (TG), total cholesterol (TCHO), and eGFR. Blood and urine samples were collected from participants in a non-fasting state and centrifuged at 3000 rpm for 20 minutes at 4°C; serum and urine were then stored at -80°C for further analysis. Blood samples were obtained from 1 ml of fasting venous blood in the morning under sterile conditions and collected in EDTA anticoagulant tubes. After collection, the supernatant was immediately centrifuged at 3000 r / min for 5 minutes at room temperature, and the supernatant was taken and frozen in a -80°C refrigerator.

[0027] Results The general characteristics of the study participants are shown in Table 1. All patients had first-onset SSNS and were not receiving any sterol treatment. Regarding serum samples, the median age of the normal group and the patient group was 58.9 (95% confidence interval [CI]: 51.4-66.4) and 37.0 (95% CI, 27.0 to 77.5) months, respectively. For urine samples, the median age of the normal group and the patient group was 72.5 (95% CI: 56.0-89.1) and 31.5 (95% CI, 48.0-92.5) months, respectively. In the normal group, the male-to-female ratio in serum and urine samples was approximately 1:1, while in the SSNS group, the male-to-female ratio was approximately 1.6:1, which is similar to other studies. There was no statistically significant difference in age or gender between the two groups (p value>0.05).

[0028] Table 1. Clinical data of patients at baseline

[0029]

[0030] Step 2. Sample preparation for metabolite analysis and non-targeted metabolite detection analysis using UHPLC-QTOF-MS

[0031] Serum samples (200 μL) were extracted using 800 μL HPLC grade methanol (methanol:H2O=4:1, v / v) in 1.5 mL / 2 mL Eppendorf tubes. Vortex for 1 min (<=1 min) at 4-8°C, then incubate at -80°C for 2 hours or overnight. Centrifuge at 14000g (or maximum speed) at 4°C for 20 min using a refrigerated centrifuge, then transfer the same volume of supernatant to a new Eppendorf tube. Avoid touching the bottom pellet when transferring the supernatant, and measure the same volume for other samples in the same group. Speedvac / lyophilize or dry to fine pellets using N2 without heating (or room temperature). The extracted metabolites were dissolved in 100 μL 80% methanol and analyzed by LC-MS system.

[0032] The analysis was performed using an LC-MS system including an Agilent 1290 Infinity II UHPLC system and an Agilent 6545 QTOF / MS (Agilent). Chromatographic separation was achieved on an ACQUITY UPLC BEH Amide column (100 mm × 2.1 mm, 1.7 μm). The mobile phase consisted of 15 mM ammonium acetate, 0.3% NH3·H2O in water and 15 mM ammonium acetate, 0.3% NH3·H2O in 9:1 acetonitrile / water (B) at a flow rate of 0.3 ml / min. 95% of phase B was eluted for 1 minute, and phase B was slowly changed from 95% to 50% within 1 to 8 minutes, and 50% phase B was maintained for 3 minutes, followed by a gradient increase of phase B to 95% within 0.5 minutes and maintained for 1.5 minutes. The injection volume was 5 μL.

[0033] Mass spectrometry data were acquired using electrospray ionization in positive and negative ion modes at 200–1200 m / z. Other mass spectrometry settings included: sheath gas temperature 300 °C, sheath gas flow rate 11 L / min, VCap 3500 V, capillary 0.09 mA, nozzle voltage 0 V, gas temperature 275 °C, fragmentor 150 V, and skimmer 65 V. Raw data were processed using Profinder 10.0 (Agilent) with peak detection, alignment, and integration. All metabolite variables were obtained from the UHPLC-QTOF-MS dataset. T-tests, Wilcoxon tests, and analysis of variance (ANOVA) tests were performed between two groups or multiple groups, and categorical variables were compared using the chi-square test. Principal component analysis (PCA) and partial least squares discriminant analysis (OPLS-DA) were performed by MetaboAnalyst version 4.0 (http: / / www.metaboanalyst.ca). Data were scaled to unit variance before PCA. Metabolite abundance was log-transformed before pathway analysis. All statistical tests were two-sided and significance was set at P < 0.05.

[0034] The results are as follows Figure 1 As shown in the figure, in the UHPLC-QTOF-MS data set, a total of 6870 metabolite features were detected in the positive ion mode and 12760 metabolite features were detected in the negative ion mode. After data annotation, we obtained 178 metabolite features in the positive ion mode and 163 metabolite features in the negative ion mode. We applied the principal component analysis (PCA) model and orthogonal partial least squares discriminant analysis (OPLS-DA) to determine whether the metabolic characteristics of SSNS patients were different from those of the normal group. In the PCA model, the two groups of serum samples were Figure 1 A) and negative ion mode ( Figure 1 C) have obvious separation trends. The OPLS-DA score chart shows that the two groups of serum samples have obvious separation trends ( Figure 1 B, D). However, there was no clear trend of separation between the two groups of urine samples ( Figure 2 ). Therefore, serum samples were further analyzed for differential analysis. In addition, after one week of hormone treatment, PCA and OPLS-DA analysis showed that there was no difference in the metabolic profile of urine between the two groups.

[0035] In order to explore the metabolic pathways associated with SSNS disease, such as Figure 3As shown in the figure, for the positive ion mode, the main enriched pathways of metabolites in SSNS cases include glycine, serine and threonine metabolism, alanine, aspartate and glutamate metabolism, taurine and hypotaurine metabolism, phenylalanine metabolism and tryptophan metabolism. For the negative ion mode, the main enriched pathways of metabolites in SSNS cases include alanine, aspartate and glutamate metabolism, glycine, serine and threonine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, histidine metabolism and arginine biosynthesis pathways ( Figure 4 ).

[0036] In the positive ion mode (111) and negative ion mode (113), a total of 22 significantly upregulated and 172 significantly downregulated metabolites were identified in the SSNS group. Among these changed metabolites, 3 different upregulated metabolites and 27 different downregulated metabolites were verified in both positive and negative ion modes, and the significantly different metabolites were further enriched for disease pathways ( Figure 5 A / B). Figure 5 As shown in C / D, chronic renal failure was predicted in both positive and negative ion modes. These results indicate that differential metabolites are related to renal function. These different metabolites were further analyzed for pathways. The results showed that the main differences between children with SSNS and healthy controls were concentrated in amino acid metabolism and TCA cycle ( Figure 5 E / F).

[0037] The present invention performed hierarchical cluster analysis (HCA) and area under the curve (AUC) analysis to globally visualize the significantly altered metabolites. Metabolites showing significant patterns in serum levels between the normal group and the SSNS group were identified as potential biomarkers for early detection of SSNS. Among them, according to HCA cluster and AUC analysis (AUC = 1, log2 (FC) > 5) ( Figure 6 ) The first eight metabolites were selected, namely Dulcitol, D-Sorbitol, D-Mannitol, XMP, NADPH, NAD, Bilirubin, and α-KG.

[0038] Step 3. Estimation of causal effect

[0039] Causal effects present stable and explainable relationships between variables, but they are difficult to determine. In contrast, statistical methods and machine learning methods can study the correlation of variables. However, although correlation can be found, it does not explain the causal relationship between variables. In some cases, the observed association is a "false association", which means that both variables are related to a third variable, which is omitted in the model. In addition, selection bias occurs when the selection of data sets and samples changes. To overcome this shortcoming, this application applies causal inference, which was born and developed from multiple different research fields, including computer science, econometrics, biostatistics, and epidemiology (Hernan, Robins 2020; Pearl, Mackenzie 2018). More specifically, the DoWhy model is applied, which can specify and test causal hypotheses (Glass et al., 2013; Sharma, Kiciman 2020). The inference of causal relationships can provide a more reliable reference beyond the traditional statistical methods used.

[0040] To verify the hypothesis, a causal inference model, namely DoWhy model analysis, was applied to construct a multi-factor correlation (Dulcitol, D-Sorbitol, D-Mannitol, XMP, NADPH, NAD, Bilirubin, α-KG) to evaluate the causal relationship between the selected variables and SSNS. It was assumed that all these variables had a causal effect on SSNS, and the strength of the causal effect relationship was studied. All data were preprocessed to the range by min-max normalization, because the values ​​of some variables were several orders of magnitude larger than others, which could lead to incorrect estimates of the contribution of other variables. All variables showed a significant causal effect on SSNS ( Figure 7 ). Four variables have positive causal effects, namely Dulcitol, D-Sorbitol, D-Mannitol, and XMP, while the other four variables have negative causal effects, namely NADPH, NAD, Bilirubin, and α-KG.

[0041] Example 2 Quantitative verification of targeted metabolites

[0042] Step 1. New study population and sample collection, sample pretreatment

[0043] Serum samples from 41 SSNS patients and 43 healthy children were collected as controls. Clinical information was collated, and serum samples were collected and pretreated in the same manner as before. The median age of the normal group and the patient group was 61.0 (95% confidence interval [CI]: 45.0-84.0) and 41.0 (95% CI, 28.0 to 66.0) months, respectively. In the normal group, the male-to-female ratio in serum and urine samples was approximately 1.6:1, while in the SSNS group, the male-to-female ratio was approximately 2.1:1, which is similar to other studies. There was no statistically significant difference in age or gender between the two groups (p value>0.05).

[0044] Table 2. Baseline clinical data of patients in the validation set

[0045]

[0046]

[0047] Step 2. Absolute quantification of the selected markers using triple quadrupole

[0048] MS technology provides the ability to detect and quantify a large number of metabolites. Currently, the quantification of target metabolites is performed on an Agilent 1290 Infinity II LC and Agilent 6495 Triple Quadrupole MS system (Agilent Technologies, Palo Alto, CA, USA) equipped with an Agilent Jet Stream ESI source. Mass Hunter Workstation (Ver B.06.00, Agilent Technologies) software is used for data acquisition and analysis. According to the biophysical and chemical differences of differential metabolites, three different methods were selected for detection.

[0049] 2.1 After the metabolites were extracted by methanol, D-Mannitol (mannitol, 182.0788 g / mol, C6 H 14 O6,CAS 69-65-8), D-Sorbitol (Sorbitol, 182.0788 g / mol, C6H 14 O6,CAS 50-70-4),Dulcitol(galactitol,182.0788g / mol,C6H 14 O 6, CAS 608-66-2), Alpha-ketoglutarate (ketoglutaric acid, 146.1g / mol, C5H6O5, CAS 328-50-7), Bilirubin (bilirubin, 584.2661g / mol, C 33H 36 N4O6,CAS 635-65-4).

[0050]

[0051] 2.2 After the metabolites were extracted by methanol, NAD (nicotinamide adenine dinucleotide, 663.1275 g / mol, C 21 H 27 N7O 14 P2,CAS 53-84-9), NADPH (reduced nicotinamide adenine dinucleotide phosphate, 745.0947 g / mol, C 21 H 30 N7O 17 P3, CAS 53-57-6)

[0052]

[0053] like Figure 8 As shown, consistent with the non-targeted metabolic profile, the levels of D-mannitol, dulceritol, and D-sorbitol were higher in the SSNS group, whereas the levels of NADPH, NAD, bilirubin, and α-ketoglutarate analogs were lower in the SSNS group.

Claims

1. Use of a biomarker in the preparation of a reagent for early diagnosis of nephrotic syndrome, characterized in that: The biomarkers are galactitol, sorbitol, mannitol, nicotinamide adenine dinucleotide, ketoglutaric acid, bilirubin or reduced nicotinamide adenine dinucleotide phosphate; early diagnosis of nephrotic syndrome is performed by examining the expression levels of the markers in the blood.

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