Marker combination, combined reagent and kit for early risk assessment of caries of low-age children and application of marker combination, combined reagent and kit in construction of early risk assessment model of caries of low-age children

Abstract

The invention provides a marker combination, a combined reagent and a kit for early risk assessment of caries of young children and application of the marker combination, the combined reagent and the kit in construction of an early risk assessment model of caries of young children, and belongs to the technical field of oral preventive medicine and oral public health. Saliva samples of the young children are researched by adopting proteomics and metabonomics, and 2-oxoadipic acid, enoyl coenzyme A hydratase and kynurenase can be used as a marker combination for evaluating early caries risk of the young children. According to the abundance reference indexes of 2-oxoadipic acid, enoyl coenzyme A hydratase and kynureninase, an early risk assessment model for the caries of the low-age children is constructed. The early risk assessment model for the caries of the young children, which is constructed by the method disclosed by the invention, has relatively good stability, sensitivity and specificity, and lays a solid foundation for taking effective intervention measures to realize comprehensive prevention and control of caries.

Description

Technical Field

[0001] The present invention belongs to the technical field of oral preventive medicine and oral public health, and in particular relates to a marker combination, a combined reagent, a kit for early risk assessment of dental caries in young children, and their application in constructing an early risk assessment model for dental caries in young children. Background Art

[0002] Dental caries in young children is a common and frequently occurring oral disease in children, and has become one of the most common health problems in children worldwide. The results of the 2015 National Oral Health Epidemiological Survey showed that the dental caries rates of children aged 3, 4, and 5 were as high as 50.8%, 63.6%, and 71.9%, respectively, which are at a relatively high level and have increased significantly compared to 10 years ago. Untreated dental caries can cause abnormal development and eruption of permanent teeth, affect children's chewing and pronunciation functions, and affect their eating and language abilities, which in turn can lead to a series of social and psychological problems, and also impose a significant economic burden on their families and society. The harm of dental caries in young children to children's growth, development, and physical and mental health cannot be ignored. According to the four-factor theory of caries, caries involves the combined action of multiple factors. Existing studies mostly focus on single-omics or single-factor analysis. For example, the caries status (Cariostat) detection system only assesses the risk of caries by pH value, or only constructs a caries risk assessment model through proteomics and metabolomics. It fails to combine multi-omics technology to associate the dynamic changes of caries in young children with different types of biological markers. Therefore, it can only reveal the biological molecular changes at a certain level. The biological information obtained is relatively one-sided, and it is difficult to comprehensively and comprehensively reflect the interaction mechanism between the host and microorganisms. The test efficiency of the model is not high. In addition, the construction of existing diagnostic models is mostly based on biological markers screened by cross-sectional studies, and there is a lack of long-term longitudinal follow-up cohort studies. Models constructed based on cross-sectional study results are difficult to achieve dynamic monitoring and risk assessment of caries status. The existing model was constructed by comparing healthy people with young children with caries. Therefore, the model only focuses on the state after caries occurs. It analyzes the saliva of children to diagnose whether they have caries. However, it cannot play a role in risk assessment and prevention before caries occurs in young children. Therefore, it has certain limitations in detection effect. Therefore, it is urgent to identify and monitor the occurrence and development of children's caries early so as to take targeted comprehensive prevention and control measures.

[0003] Preschool children are often afraid or uncooperative, making dental caries inspection and necessary group monitoring difficult. However, saliva has the inherent advantages of simple sampling, painless and non-invasive, which is more friendly and comfortable for preschool children, and easy to gain children's trust and cooperation. Saliva can comprehensively and comprehensively reflect the oral health status. The detection of important biological markers such as proteins / peptides and microorganisms contained in saliva is an important way to solve the problem of early detection and diagnosis of dental caries, and is a key method to achieve rapid dental caries monitoring, risk assessment and family self-care. The changes in the composition of proteins and metabolites in children's mouths, represented by saliva components, are closely related to age, and these changes precede the onset of dental caries symptoms. This also means that children's oral proteins and metabolites need to be carefully monitored and analyzed in order to accurately predict the risk of dental caries. At the same time, many parents and the public lack awareness of the prevention of dental caries in children and do not understand the importance of early monitoring and early warning. This has led to many children seeking medical help only when dental caries symptoms are obvious, missing the best time for prevention and intervention. At present, due to the uneven distribution of medical resources, in some areas, especially in remote mountainous areas, there is a lack of professional dental specialists and necessary medical equipment, making regular dental examinations and dental caries risk assessment difficult to achieve. The current dental caries risk assessment model cannot accurately predict whether an individual will develop dental caries within a certain period of time, and there is also a lack of quantitative analysis and evaluation methods. Therefore, it is difficult to achieve early detection and early warning of the risk of dental caries in young children.

[0004] The existing dental caries diagnosis model based on salivary biochemical testing is to collect saliva samples, compare the differences in pH, total protein and ion concentration, analyze the correlation between salivary biochemical indicators and dental caries status, and establish a dental caries diagnosis model. This model distinguishes dental caries patients from healthy children with a certain degree of accuracy. However, this model can only diagnose after dental caries occur, but cannot detect and prevent dental caries before they occur. It still cannot meet the needs of comprehensive prevention and control in the field of oral preventive medicine. The currently more commonly used dental caries detection method is to assess the risk of dental caries by the color corresponding to the pH value of the culture medium, and no quantitative analysis can be performed. In addition, this method is subjectively evaluated by the examiner, which has certain requirements on the technical level of the operator, and the sensitivity and specificity of the detection still have room for improvement.

[0005] Multi-omics analysis refers to the normalization and comparative analysis of data sources from different omics, the establishment of relationships between data from different groups, and the comprehensive and in-depth interpretation of biological processes from the gene, transcription, protein and metabolic levels by integrating multi-omics data, so as to have a more comprehensive understanding of biological systems. Identifying multi-omics markers related to dental caries in young children is conducive to further understanding the mechanism of dental caries, and is also of great significance for the early detection and intervention of diseases. Summary of the invention

[0006] In view of this, the purpose of the present invention is to provide a marker combination, a combination reagent, a kit for early risk assessment of dental caries in young children, and their application in constructing an early risk assessment model for dental caries in young children. The present invention focuses on painless, non-invasive and easy-to-obtain saliva samples, and screens salivary biological markers and functional pathways related to the dynamic changes of dental caries in young children through multi-omics joint analysis technologies such as proteomics and metabolomics, constructs a dental caries risk assessment model with good stability, sensitivity and specificity, realizes rapid detection, risk assessment and family self-care of dental caries in young children, and lays a solid foundation for taking effective intervention measures to achieve comprehensive prevention and control of dental caries.

[0007] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0008] The present invention provides a marker combination for early risk assessment of dental caries in young children, wherein the marker combination comprises 2-oxoadipate, enoyl-CoA hydratase and kynurenine enzyme.

[0009] The present invention provides a combined reagent for early risk assessment of dental caries in young children, wherein the combined reagent comprises reagents for detecting abundance reference indicators of 2-oxoadipate, enoyl-CoA hydratase and kynurenine aminotransferase.

[0010] Preferably, the abundance reference index of 2-oxoadipic acid is obtained by a metabolomics technique based on high-throughput mass spectrometry, and the abundance reference indexes of enoyl-CoA hydratase and kynurenine aminotransferase are obtained by DIA proteomics technique detection.

[0011] Preferably, the abundance reference index is obtained based on the logarithmic value with a base of 2 of the sample ion current intensity during the detection process.

[0012] The present invention provides a kit for early risk assessment of dental caries in young children, and the kit comprises the combined reagent.

[0013] The present invention also provides the use of the marker combination, the combined reagent or the kit in constructing an early risk assessment model for dental caries in young children.

[0014] Preferably, the method for constructing an early risk assessment model for dental caries in young children comprises the following steps:

[0015] (1) Collecting samples and pre-processing them;

[0016] (2) Proteomics detection using liquid chromatography-mass spectrometry technology based on data-independent acquisition; non-targeted metabolomics detection using liquid chromatography-mass spectrometry technology using the Q-Exactive mass spectrometry detection platform;

[0017] (3) Substitute the results of proteomics and non-targeted metabolomics into the decision tree of the saliva multi-omics marker analysis model to obtain an early risk assessment model for dental caries in young children:

[0018] When the abundance reference index of 2-oxoadipic acid is greater than 24.46, the abundance reference index of enoyl-CoA hydratase is greater than 7.91, and the abundance reference index of kynurenine aminotransferase is greater than 7.23, it indicates a high risk of dental caries; the rest are not at a high risk of dental caries.

[0019] Preferably, the sample is saliva.

[0020] Preferably, the pretreatment method is to centrifuge the sample to obtain the supernatant.

[0021] Preferably, the centrifugal temperature is 2-6°C, the centrifugal speed is 8000-12000g, and the centrifugal time is 5-15min.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] (1) The present invention uses proteomics and metabolomics to study saliva samples of young children, and finds that 2-oxoadipate, enoyl-CoA hydratase and kynurenine enzyme have an important influence on the occurrence of dental caries in young children, indicating that 2-oxoadipate, enoyl-CoA hydratase and kynurenine enzyme can be used as a marker combination to assess the early risk of dental caries in young children. The present invention constructs a decision tree for the analysis model of multi-omics markers of saliva by determining the abundance reference index of 2-oxoadipate, enoyl-CoA hydratase and kynurenine enzyme, and the decision tree for the analysis model of multi-omics markers of saliva can be used to assess the early risk of dental caries in young children.

[0024] (2) The present invention verifies the dental caries risk assessment model constructed by the present invention based on a longitudinal follow-up cohort study with a large sample size and little influence from individual factors. The results show that the dental caries risk assessment model for young children constructed by the present invention has good test efficiency, which provides a new idea for constructing a dental caries risk assessment model for young children.

[0025] (3) The dental caries risk assessment model constructed by the present invention has good stability, sensitivity and specificity. The dental caries risk assessment model constructed by the present invention can achieve early diagnosis of dental caries in young children through rapid and more accurate and effective detection and analysis, and take prevention and control measures to move the threshold forward, which is helpful for the improvement and optimization of family self-care methods and lays the foundation for the formulation of accurate prevention and control strategies for dental caries in young children. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is an overall flow chart of the technical solution of the present invention;

[0027] Figure 2 Model decision tree for salivary multi-omics marker analysis;

[0028] Figure 3 Flow chart for proteomics assay;

[0029] Figure 4 Flow chart for non-targeted metabolomics assay;

[0030] Figure 5 to Figure 7 Multi-omics analysis of saliva samples combining proteomics and metabolomics, including Figure 5 The tryptophan-kynurenine metabolic pathway was a significantly enriched pathway involved in the changes of caries status; Figure 6 The metabolites 2-oxoadipate, protein enoyl-CoA hydratase and kynurenine in the tryptophan-kynurenine metabolic pathway and their changing trends in different caries states; Figure 7 This is the receiver operating characteristic curve and its area under the curve of the salivary multi-omics marker analysis model applied to early caries risk assessment in young children. DETAILED DESCRIPTION

[0031] The present invention provides a marker combination for early risk assessment of dental caries in young children, wherein the marker combination comprises 2-oxoadipate, enoyl-CoA hydratase and kynurenine enzyme.

[0032] The present invention provides a combined reagent for early risk assessment of dental caries in young children, wherein the combined reagent comprises reagents for detecting abundance reference indicators of 2-oxoadipate, enoyl-CoA hydratase and kynurenine aminotransferase.

[0033] In the present invention, the abundance reference index of 2-oxoadipic acid is obtained by a metabolomics technique based on high-throughput mass spectrometry, and the abundance reference index of enoyl-CoA hydratase and kynurenine enzyme is obtained by DIA proteomics technique detection; the abundance reference index is obtained based on the logarithmic value of the sample ion current intensity with a base of 2 during the detection process, and can reflect the relative expression levels of proteins and metabolites.

[0034] The present invention provides a kit for early risk assessment of dental caries in young children, the kit comprising the combined reagent, which is a reagent for detecting the abundance reference index of 2-oxoadipate, enoyl-CoA hydratase and kynurenine aminotransferase.

[0035] The present invention also provides the use of the marker combination, the combined reagent or the kit in constructing an early risk assessment model for dental caries in young children.

[0036] In the present invention, the method for constructing an early risk assessment model for dental caries in young children comprises the following steps:

[0037] (1) Collecting samples and pre-processing them;

[0038] (2) Proteomics detection using liquid chromatography-mass spectrometry technology based on data-independent acquisition; non-targeted metabolomics detection using liquid chromatography-mass spectrometry technology using the Q-Exactive mass spectrometry detection platform;

[0039] (3) Substitute the results of proteomics and non-targeted metabolomics into the decision tree of the saliva multi-omics marker analysis model to obtain an early risk assessment model for dental caries in young children:

[0040] When the abundance reference index of 2-oxoadipic acid is greater than 24.46, the abundance reference index of enoyl-CoA hydratase is greater than 7.91, and the abundance reference index of kynurenine aminotransferase is greater than 7.23, it indicates a high risk of dental caries; the rest are not at a high risk of dental caries.

[0041] In the present invention, samples are collected and pretreated. The method for collecting samples is to collect saliva samples from preschool children more than 1 hour after breakfast, rinse the mouth with clean water before collecting saliva, and discard those mixed with blood; collect 1-5mL of whole saliva, preferably 2-4mL, and more preferably 3mL; mark the sample number and place it in a centrifuge tube rack, store it in a foam plastic box with ice packs or crushed ice, and transport it to the laboratory as soon as possible for centrifugal pretreatment; the method for pretreating the sample is to set the centrifuge to precool to 2-6°C, preferably 3-5°C, and more preferably 4°C; the centrifugal speed is 8000-12000g, preferably 9000-11000g, and more preferably 10000g; the centrifugal time is 5-15min, preferably 7-13min, and more preferably 10min, take the saliva supernatant, and put the saliva supernatant in a 1.5mL ep tube and a 200μL ep tube respectively.

[0042] In the present invention, liquid chromatography-mass spectrometry technology based on data-independent acquisition is used for proteomics detection; the Q-Exactive mass spectrometry detection platform is used to perform non-targeted metabolomics detection using liquid chromatography-mass spectrometry technology; the present invention uses liquid chromatography-mass spectrometry technology based on data-independent acquisition to perform proteomics detection on pretreated saliva samples, and the abundance reference indicators of enoyl-CoA hydratase and kynurenine amino acidase in the pretreated samples can be obtained; the Q-Exactive mass spectrometry detection platform is used to perform non-targeted metabolomics detection using liquid chromatography-mass spectrometry technology, and the abundance reference indicator of 2-oxoadipic acid in the pretreated samples can be obtained.

[0043] In the present invention, the detection and analysis results of proteomics and non-targeted metabolomics are substituted into the decision tree of the saliva multi-omics marker analysis model to obtain an early risk assessment model for dental caries in young children; the decision tree of the saliva multi-omics marker analysis model is as follows: Figure 2 As shown in the figure, when the abundance reference index of 2-oxoadipic acid is greater than 24.46, the abundance reference index of enoyl-CoA hydratase is greater than 7.91, and the abundance reference index of kynurenine aminotransferase is greater than 7.23, it indicates a high risk of dental caries; the rest are not at a high risk of dental caries.

[0044] The technical solutions provided by the present invention are described in detail below in conjunction with the embodiments, but they should not be construed as limiting the protection scope of the present invention.

[0045] Example

[0046] Saliva samples of preschool children were collected between 9:00 and 11:00 in the morning, and the collection time should be ensured to be at least 1 hour after breakfast. Before collecting saliva, rinse your mouth with clean water, and discard any blood stains; collect about 3 mL of whole saliva, mark the sample number and place it in a centrifuge tube rack, store it in a foam plastic box with ice packs or crushed ice, and transport it to the laboratory as soon as possible for centrifugal pretreatment. The pretreatment method is to precool the centrifuge to 4°C, set the speed to 10000g, centrifuge for 10 minutes, and place the saliva supernatant in 1.5 mL ep tubes and 200 μL ep tubes to obtain pretreated saliva samples.

[0047] The pre-treated saliva samples were subjected to proteomics detection using liquid chromatography-mass tandem spectrometry (LC-MS / MS) based on data independent acquisition (DIA) (operation flow see Figure 3) to obtain the abundance reference indexes of enoyl-CoA hydratase and kynurenine amino acidase in the pretreated samples; non-targeted metabolomics detection was performed using liquid chromatography-mass spectrometry technology using the Q-Exactive mass spectrometry detection platform (operation process see Figure 4 ), obtain the abundance reference index of 2-oxoadipic acid in the pretreated sample, the specific method is as follows:

[0048] 1. Data-independent acquisition of liquid chromatography-mass spectrometry for proteomics detection

[0049] 1.1 Sample preparation

[0050] The sample pretreatment includes protein extraction, denaturation, reduction alkylation, enzymatic hydrolysis and peptide desalting. The present invention uses the iST sample pretreatment kit (PreOmics, Germany) to pretreat tissue samples. Grind with liquid nitrogen, take an appropriate amount of sample, add 50 μL of lysis buffer, and heat at 95°C and 1000 rpm for 10 minutes. The sample is cooled to room temperature, and trypsin digestion buffer is added, and incubated at 37°C and 500 rpm for 2 hours. Add stop buffer to terminate the enzymatic hydrolysis reaction. The iST cartridge in the kit is used to desalt the peptides, and 2×100 μL elution buffer is used for elution. The eluted peptides are vacuum-dried and stored at -80°C.

[0051] 1.2 Establishing a spectral database

[0052] 1) High pH reverse phase separation

[0053] All sample peptide mixtures were redissolved in buffer A (buffer A: 20 mM ammonium formate aqueous solution, adjusted to pH 10.0 with ammonia) and then separated at high pH using an Ultimate 3000 system (ThermoFisher scientific, MA, USA) connected to a reverse column (XBridge C18 column, 4.6 mm × 250 mm, 5 μm, (Waters Corporation, MA, USA). The separation used a linear gradient from 5% B to 45% B in 40 min (B: 80% ACN with 20 mM ammonium formate, adjusted to pH 10.0 with ammonia). The column was equilibrated at the initial conditions for 15 min, the column flow rate was maintained at 1 mL / min, and the column temperature was maintained at 30 °C. Six fractions were collected and each fraction was dried in a vacuum concentrator for later use.

[0054] 2) Low pH nano-HPLC-MS / MS analysis (DDA qualitative library construction)

[0055] The desalted and lyophilized peptides were redissolved in solvent A (A: 0.1% formic acid in water) and analyzed by LC-MS / MS equipped with an online nanospray ion source. The entire system was an Orbitrap Lumos mass spectrometer (Thermo Fisher Scientific, MA, USA) connected in series with the EASY-nLC 1200 system. A total of 3 μL was loaded (analytical column Acclaim PepMap C18, 75 μm × 25 cm) and separated by a 120 min gradient: 5% B to 35% B (B: 0.1% formic acid ACN solution). The column flow rate was controlled at 200 nL / min and the electrospray voltage was 2 kV.

[0056] The Orbitrap Lumos mass spectrometer was operated in data-dependent acquisition mode, automatically switching between MS and MS / MS acquisition. The mass spectrometry parameters were set as follows: (1) MS: scan range (m / z): 350–1500; resolution: 120,000; AGC target: 4e5; maximum injection time: 50 ms; dynamic exclusion time: 30 s; (2) HCD-MS / MS: resolution: 15,000; AGC target = 5e4; maximum injection time: 35 ms; collision energy: 32.

[0057] 3) Search the database

[0058] The raw data were merged and analyzed by Spectronaut X (Biognosys AG). The database used was Uniprot or the provided database. In addition, the contamination sequence library was searched to determine whether the sample was contaminated. Trypsin enzymatic digestion was set. The search parameters were as follows: fixed modification: Carbamidomethyl (C), variable modification: methionine oxidation. The false positive rate (FDR) at the parent ion and peptide levels was set to 1%.

[0059] 1.3 DIA data acquisition

[0060] Each sample was added with 30 μL solventA (A: 0.1% formic acid in water) to make a suspension, 9 μL was taken out and added with 1 μL 10×iRT peptide, mixed and separated by nano-LC, and analyzed by online electrospray tandem mass spectrometry. The whole experimental system was an Orbitrap Lumos mass spectrometer (Thermo Fisher Scientific, MA, USA) connected to the EASY-nLC 1200 system. A total of 3 μL was loaded (analytical column AcclaimPepMap C18, 75 μm×25 cm), and the gradient separation was 120 min: 5% B to 35% B (B: 0.1% formic acid ACN solution). The column flow was controlled at 200 nL / min and the electrospray voltage was 2 kV.

[0061] The mass spectrometry parameters were set as follows:

[0062] (1) MS: scan range (m / z): 350-1500; resolution: 120,000; AGC target: 4e6; maximum injection time: 50 ms; (2) HCD-MS / MS: resolution: 30,000; AGC target: 1e6; collision energy: 32; energy increase: 5%; (3) variable window acquisition, 60 windows were set, overlapping serial ports were set, and each window overlapped 1m / z.

[0063] 2. Using the Q-Exactive mass spectrometry platform, liquid chromatography-mass spectrometry was used for non-targeted metabolomics detection

[0064] 2.1 Metabolite extraction

[0065] 1) Pipette 100 μL of sample into a 2 mL centrifuge tube;

[0066] 2) Add 400 μL of pre-cooled methanol:acetonitrile (1:1, v / v) and vortex for 30 s;

[0067] 3) Freeze in a -20℃ refrigerator for 30 min;

[0068] 4) Centrifuge at 12000 rpm and 4°C for 10 min, take 400 μL of the supernatant and concentrate to dryness under vacuum;

[0069] 5) Add 150 μL 50% methanol (containing 5 ppm 2-chlorophenylalanine) to dissolve, and vortex for 30 seconds;

[0070] 6) Centrifuge at 12000 rpm and 4°C for 10 min, filter the supernatant through a 0.22 μm filter membrane, and add the filtrate to the test bottle;

[0071] 7) Take 10-20 μL of each sample filtrate and mix them into a QC sample for evaluating instrument stability and data reliability.

[0072] 2.2 Chromatographic method:

[0073] Using ACQUITY UPLCH SS T3 Columns Flow rate 0.4 mL / min, column temperature 40 °C, autosampler 8 °C, injection volume 2 μL.

[0074] Positive and negative mode mobile phase: mobile phase A is 0.1% formic acid water, mobile phase B is acetonitrile (containing 0.1% formic acid), elution gradient is shown in Table 1:

[0075] Table 1 Elution gradient in positive and negative modes

[0076]

[0077]

[0078] 2.3 Mass spectrometry method:

[0079] Thermo Orbitrap Exploris 120 mass spectrometer was controlled by Xcalibur software (version: 4.7, Thermo) to collect DDA mass spectrometric data in positive and negative ion modes, respectively. HESI source, spray voltage 3.5 kV / -3.0 kV, sheath gas 40 arb, auxiliary gas 15 arb, capillary temperature 325 ° C, auxiliary gas temperature 300 ° C, primary resolution 60,000, scanning range 100-1000 m / z, AGC Target Standard, Max IT 100 ms, screening the top 4 ions for secondary fragmentation, dynamic exclusion time of 8 s, secondary resolution 15,000, HCD collision energy 30%, AGC Target Standard, Max IT Auto.

[0080] All samples to be tested and QC samples were loaded into the machine according to the above-mentioned chromatography and mass spectrometry methods. Before the formal injection, 2 to 4 QC samples were injected to balance the system. During the injection process, one QC sample was injected for every 5 to 10 samples for subsequent data evaluation and quality control.

[0081] 3. Obtaining proteomics analysis results

[0082] First, quality control analysis was performed on the original data (Raw Data) corresponding to the samples, and differential proteins were screened on this basis. Principal component analysis, correlation analysis, expression pattern cluster analysis, etc. were performed on the difference comparison group data.

[0083] Then, functional annotation analysis of the differentially expressed proteins was performed, including GO analysis, KEGG Pathway analysis, protein interaction, etc. Through this series of analyses, key proteins and their functions or pathways were selected.

[0084] 4. Obtaining metabolomics analysis results

[0085] 4.1 Metabolite library search

[0086] Import the .raw format data into the commercial software Compound Discoverer TM3.3 (version 3.3.2.31, Thermo, Waltham, USA), based on the software's new peak detection and peak quality scoring algorithm, peak extraction, alignment, correction and other operations are performed. The unique peak quality rating calculation and filter (Peak rating calculation and filter) greatly reduce the interference of background peaks and low-quality peaks. The peaks that are not detected in more than 50% of the QC samples are filtered, and the undetected peaks are filled with missing values ​​based on the software Fill Gaps algorithm, and the Sum total peak area is normalized. The identification of metabolites is based on the self-built library, mzCloud online library (https: / / www.mzcloud.org / ), LIPID MAPS (https: / / www.lipidmaps.org / ), HMDB (https: / / hmdb.ca / ), MoNA (https: / / mona.fiehnlab.ucdavis.edu / ) and NIST_2020_MSMS spectral library. The MS1 mass tolerance MassTolerance is set to 15ppm and the MS2 Match FactorThreshold is set to 50.

[0087] 4.2 Bioinformatics analysis

[0088] (1) Expression abundance analysis

[0089] The metabolite abundance values ​​were plotted using ggplot2 (V3.4.1) to generate expression abundance density maps and violin plots. The metabolite abundance values ​​were clustered using the Pheatmap package (V1.0.12) in R. At the same time, samples and metabolites were bidirectionally clustered and heat maps were drawn to display the abundance of sample metabolites.

[0090] (2) Comparison of the two groups

[0091] The R software package Ropls was used to perform principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) on the sample data. Score plots, loading plots, and S-plots were drawn to show the differences in metabolite composition between samples. The permutation test method was used to test the overfitting of the model. R2X and R2Y represent the explanatory rate of the constructed model for the X and Y matrices, respectively, and Q2 indicates the predictive ability of the model. The closer their values ​​are to 1, the better the fit of the model is, and the more accurately the samples in the training set can be divided into their original attributes. The pvalue value was calculated according to the statistical test, the variable projection importance (VIP) was calculated by the OPLS-DA dimensionality reduction method, and the fold change (FC) was calculated to calculate the difference between the two groups. The intensity and explanatory power of the content of each metabolite component on the classification and discrimination of the samples were measured to assist in the screening of marker metabolites. When the pvalue value is <0.05 and the VIP value is >1, the metabolite molecule is considered to be statistically significant.

[0092] The Pheatmap package (V1.0.12) in R was used to perform cluster analysis on the abundance values ​​of differential metabolites, and heat maps and trend analysis graphs were drawn. VennDiagram (V1.7.3) and UpSetR (V1.4.0) were used to draw Venn diagrams and Upset diagrams for the differential substances compared between two different groups. Corrplot (V4.0.3) was used to perform correlation analysis on the differential metabolites. ggplot2 (V3.4.1) was used to draw box plots and violin plots of differential metabolites to show the abundance of each differential substance between different groups. The differential metabolite results were further analyzed by machine learning (mlr3verse, V0.2.7) and ROC curve drawing (pROC, V1.18.2) to obtain key product information in the differential set.

[0093] Functional analysis of differential metabolites was mainly carried out by performing KEGG enrichment analysis on differential substances through clusterProfiler (V4.6.0) to obtain significantly enriched metabolic pathway information, and the overall change differential abundance score of all differential metabolites in a certain pathway was obtained by differential abundance score calculation, thereby capturing the average and overall change trend of all metabolites in a certain pathway and better screening key pathways.

[0094] (3) Multi-group comparison analysis

[0095] PMCMRplus (V1.9.6) was used to perform multi-group comparison statistical test analysis to obtain significant p-value results. The R software package Ropls was used to perform principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) on the sample data, and score plots, loading plots and S-plots were drawn to show the differences in metabolite composition between samples. The model was tested for overfitting using the permutation test method. R2X and R2Y represent the explanation rate of the constructed model for the X and Y matrices, respectively, and Q2 indicates the predictive ability of the model. The closer their values ​​are to 1, the better the fit of the model, and the more accurately the samples in the training set can be divided into their original affiliations. The pvalue value was calculated based on the statistical test. When the pvalue value was <0.05, it was considered that the differences in metabolite molecules between multiple groups were statistically significant.

[0096] The Pheatmap package (V1.0.12) in R was used to perform cluster analysis on the abundance values ​​of differential metabolites, and heat maps and trend analysis diagrams were drawn; VennDiagram (V1.7.3) and UpSetR (V1.4.0) were used to draw Venn diagrams and Upset diagrams for differential substances in different multi-group comparisons; corrplot (V4.0.3) was used to perform correlation analysis on differential metabolites; the results of differential metabolites were further analyzed by machine learning (mlr3verse, V0.2.7) and ROC curve drawing (pROC, V1.18.2) to obtain key product information in the differential set. Functional analysis of differential metabolites was performed, mainly through clusterProfiler (V4.6.0) to perform KEGG enrichment analysis on differential substances, and obtain significant enrichment pathway information.

[0097] According to the proteomics and metabolomics analysis results obtained in the study, the saliva multi-omics marker analysis model decision tree ( Figure 2 ), combined with the abundance reference indicators of three key markers, namely the saliva metabolite 2-oxoadipate, the saliva protein enoyl-CoA hydratase and kynurenine enzyme, among which the abundance value of the saliva metabolite 2-oxoadipate was obtained by metabolomics technology based on high-throughput mass spectrometry, and the saliva protein enoyl-CoA hydratase and kynurenine enzyme were obtained by DIA proteomics technology, so as to determine whether the individual from whom the sample was derived has a high risk of early childhood caries.

[0098] The present invention carried out a 6-month validation cohort to apply the multi-omics marker analysis model to early caries risk assessment in young children. For the specific process, see Figure 1 A total of 103 preschool children were included in the cohort. They were followed up every 3 months, and caries examinations and caries status were recorded. Saliva samples were collected and multi-omics analysis combining proteomics and metabolomics was performed. The results are shown in Figure 5 to Figure 7 .

[0099] The study found that the expression of the saliva metabolite 2-oxoadipic acid, saliva protein enoyl-CoA hydratase and kynurenine amino acidase showed a significant upward trend during the stable period of filling treatment, which was a completely different trend compared with the children in the study cohort who suffered from caries again (i.e., new caries, secondary caries or recurrent caries) at the next follow-up time point after caries treatment. The detection results of the three components of the saliva metabolite 2-oxoadipic acid, saliva protein enoyl-CoA hydratase and kynurenine amino acidase were combined, and the receiver operating characteristic (ROC) curve was drawn based on the decision tree of the salivary multi-omics marker analysis model. The area under the curve (AUC) value of the combined model of the three reached a level of more than 0.90 (the actual AUC value was 0.900826, as shown in Figure 2). Figure 7 The results show that the salivary multi-omics marker analysis model constructed based on the three salivary biomarkers of 2-oxoadipic acid, enoyl-CoA hydratase and kynurenine enzyme in saliva has good test efficiency in the application of early risk assessment of dental caries in young children, which is helpful to identify the occurrence and prognosis of dental caries and has important practical significance and application value.

[0100] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A marker combination for early risk assessment of dental caries in young children, characterized in that: The marker combination includes 2-oxoadipate, enoyl-CoA hydratase, and kynurenine enzyme.

2. A combined reagent for early risk assessment of dental caries in young children, characterized in that: The combined reagent comprises reagents for detecting the abundance reference indicators of 2-oxoadipate, enoyl-CoA hydratase and kynurenine enzyme.

3. The combined reagent according to claim 2, characterized in that: The abundance reference index of 2-oxoadipic acid is obtained by a metabolomics technique based on high-throughput mass spectrometry, and the abundance reference indexes of enoyl-CoA hydratase and kynurenine aminotransferase are obtained by DIA proteomics technique detection.

4. The combined reagent according to claim 2 or 3, characterized in that: The abundance reference index is obtained based on the logarithmic value with a base of 2 of the sample ion current intensity during the detection process.

5. A kit for early risk assessment of dental caries in young children, characterized in that: The kit comprises the combined reagent according to any one of claims 2 to 4.

6. Use of the marker combination according to claim 1, the combined reagent according to any one of claims 2 to 4, or the kit according to claim 5 in constructing an early risk assessment model for dental caries in young children.

7. The use according to claim 6, characterized in that: The method for constructing an early risk assessment model for dental caries in young children comprises the following steps: (1) Collecting samples and pre-processing them; (2) Proteomics detection using liquid chromatography-mass spectrometry technology based on data-independent acquisition; non-targeted metabolomics detection using liquid chromatography-mass spectrometry technology using the Q-Exactive mass spectrometry detection platform; (3) Substitute the results of proteomics and non-targeted metabolomics into the decision tree of the saliva multi-omics marker analysis model to obtain an early risk assessment model for dental caries in young children: When the abundance reference index of 2-oxoadipic acid is greater than 24.46, the abundance reference index of enoyl-CoA hydratase is greater than 7.91, and the abundance reference index of kynurenine aminotransferase is greater than 7.23, it indicates a high risk of dental caries; the rest are not at a high risk of dental caries.

8. The use according to claim 7, characterized in that: The sample is saliva.

9. The use according to claim 7, characterized in that: The pretreatment method is to centrifuge the sample to obtain the supernatant.

10. The use according to claim 9, characterized in that: The centrifugal temperature is 2-6°C, the centrifugal speed is 8000-12000g, and the centrifugal time is 5-15min.

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