CN116559319A - Urine metabolism small molecule combination for assisting in diagnosis of fetal intrauterine growth restriction and application thereof - Google Patents

Abstract

The invention belongs to the field of metabonomics and medicine, and discloses a urine metabolism small molecule combination for assisting in diagnosing the intrauterine growth restriction of a fetus and application thereof. The combination of the small metabolic molecules is the combination of the small metabolic molecules, namely tiglinide, choline, D-ribose, uric acid, 2-acetyl-3-ethylene-3, 4,5, 6-tetrahydropyridine, p-methyldione, propyl cyclopropylamine, cinnamoyl glycine, methionine sulfoxide, L-carnitine, hydroxypropionyl isoleucine, m-aminobenzoic acid, N-desmethyl itratriptan, uridine, acetaminophen glucuronic acid, O Luo Tiding, dehydroepiandrosterone sulfate, 5-ethyl-4-methylthiazole, 1-3-aminopropyl-4-aminobutyraldehyde, clovoxamine and methyl 2-furoate, and can be used for preparing an auxiliary diagnostic kit with limited fetal uterine growth.

Description

Urine metabolism small molecule combination for assisting in diagnosis of fetal intrauterine growth restriction and application thereof

Technical Field

The invention belongs to the field of metabonomics and medicine, and particularly relates to a urine metabolism small molecule combination related to auxiliary diagnosis of fetal intrauterine growth restriction and application thereof.

Background

Fetal growth in the mother's womb is directly related to offspring perinatal mortality. Proper fetal growth is considered a long-term health basis, and abnormalities in fetal growth may affect the risk of disease throughout the life cycle. Barker in 1995 first proposed the hypothesis of fetal origin of adult disease, who thought that changes in the intrauterine environment and the postnatal early metabolic nutritional environment would increase the risk of developing chronic disease in adulthood. Later, more and more studies have shown that intrauterine limited fetuses are more prone to neonatal complications, childhood neurological disorders, ischemic hypoxic encephalopathy and adult metabolic syndrome, such as type 2 diabetes, coronary heart disease and hypertension. Fetal growth restriction (fetal growth restriction, FGR), also known as retarded fetal growth, restricted intrauterine growth or retarded intrauterine growth, refers to abnormal fetal growth due to pathological factors such as maternal, fetal, placental, etc., and does not reach its genetic growth potential in utero, which is most manifested as an ultrasonic estimated weight or abdominal circumference of the fetus below the 10 th percentile of the corresponding gestational age. FGR mostly contains less gestational age (small for gestational age infant, SGA). The incidence rate in developed countries is 4-8% and the incidence rate in developing countries is 6-30%. The incidence rate of FGR in China is about 6.4%, and the death rate of perinatal infants is 6-9 times that of normal infants. In recent years, research has shown that intrauterine growth limitation adversely affects both pregnant women and fetuses, and is the second leading cause of perinatal death in newborns, accounting for 30% of dead-born infants, and is the most common cause of premature birth and postpartum asphyxia, and may also bring about long-term adverse consequences including cognitive impairment in childhood and increased risk of developing adult diseases (e.g., obesity, type 2 diabetes, cardiovascular disease, stroke, etc.). If the intervention is not timely carried out, the fetal late stage can be subjected to hypoxia and intrauterine death. If the fetus is always in a growth-limited state, the metabolic status will change: to save the relevant energy metabolism, only relevant organs are allowed to grow, such as the brain, while affecting other tissue muscle growth. It follows that intrauterine growth limitation has become a major public health problem to be solved urgently, and early prevention, early diagnosis and early treatment are a major and difficult medical task.

The factors responsible for FGR typically involve 3 aspects of the maternal, fetal, placental umbilical cord. The maternal factors mainly include: malnutrition, multiple pregnancy, pregnancy complications (pre-pregnancy complicated with cyanosis type heart disease, chronic kidney disease, chronic hypertension, diabetes, thyroid disease, autoimmune disease, etc.), pregnancy complications (preeclampsia, intrahepatic cholestasis of pregnancy, etc.), multiple pregnancy, etc. Fetal factors mainly include: genetic abnormalities (chromosomal, genomic, monogenic, etc.) and structural abnormalities (congenital heart disease, abdominal wall fissures, etc.). Factors of placenta umbilical cord mainly include: placenta abnormalities (outline placenta, placenta hemangioma, subcrenal hematoma, small placenta, auxiliary placenta, etc.) and umbilical cord abnormalities (single umbilical artery, umbilical cord fineness, umbilical cord torsion, umbilical cord knotting, etc.). Other factors may also cause FGR to occur: intrauterine infections (rubella, cytomegalovirus, toxoplasmosis, syphilis, etc.), environmental teratogens, the use and abuse of drugs (tobacco, alcohol, cocaine, anesthetics, etc.), and the like. In summary, the final onset of FGR should be caused by a multifactorial interaction. Regarding the pathogenesis of FGR, researchers have conducted studies around aspects such as pathophysiology for many years, but the exact pathogenesis of this disease has not yet been fully elucidated. In addition, clinical diagnosis of FGR remains difficult.

Ultrasonic and Doppler blood flow velocity measurement are the main methods for clinically screening FGR at present, and the estimated body weight of a fetus can be calculated through head circumference, abdomen circumference, femur length and double-top diameter obtained by ultrasonic, and the Doppler blood flow velocity measurement provides fetal blood flow resistance. Other measures associated with poor pregnancy outcome include middle cerebral artery pulsatility index and brain placenta ratio. In addition, growth curves currently established and published for assessing fetal size may be used to monitor and early diagnose FGR, including non-customized curves (Hadlock fetal growth curve, INTERGROWTH-21st, world health organization fetal growth curve, etc.), and customized growth curves (GROW growth curve, national institute of child health and human development fetal growth curve, and southern China population fetal growth curve, etc.). However, the detection rate of the existing screening means is low, the occurrence of iatrogenic premature labor is caused by false positive, the B-ultrasonic detection rate of low-risk pregnant women is only 15%, and most fetuses can be diagnosed only after birth. It is presently believed that maternal history, physical examination, serological screening, and uterine artery doppler screening can be used to screen FGR. However, because of the different designs of the various clinical studies, there are large differences in the evaluation of screening effects.

Metabolomics is an emerging group and aims to quantitatively analyze low molecular weight metabolites in intermediate or final products produced by all metabolic pathways in an organism, and can simultaneously acquire information on gene expression and cumulative exposure of an individual to diet, environment, physical activity or disease and interaction thereof, and the level of the information can represent the interaction between the individual and the environment. Since metabolites are the end products of complex biosynthetic and catabolic pathways, metabolite molecules perform and respond to most of the body's processes, metabolite research is considered the most informative manifestation of biological function, and metabolomics is also considered a powerful technique to study exogenous factor-stimulated phenotypic changes with early aided diagnosis more than other histology approaches. At present, metabonomics has very wide application in the medical field, such as diagnosis and screening of diseases, efficacy evaluation, drug development, monitoring of patient response to treatment, and the like. The non-targeted metabonomics is taken as one of the metabonomics, mainly comprises the steps of unbiased detection of all detected metabolite molecules in a sample, differential analysis and path analysis through a signaling method, searching for biomarkers and preliminary modeling. The use of non-targeted metabolomics provides the possibility to determine metabolic species, further look for FGR early biomarkers in maternal body fluids, and predict the risk of FGR occurrence early by some metabolite enrichment. Biomarkers such as placenta growth factor (PlGF), soluble fms-like tyrosine kinase 1 (s-Flt-1), alpha fetoprotein, etc. have been studied to demonstrate assisted diagnosis of FGR, but have limited early diagnostic capabilities (PlGF: positive likelihood ratio 1.3; s-Flt-1: positive likelihood ratio 1.4; alpha fetoprotein: positive likelihood ratio 3.7). Prenatal identification can provide targeted follow-up for patients, reducing the risk of occurrence of poor perinatal outcomes and stillbirth. The existing research data show that daily administration of low doses of acesulfame after prenatal identification can halve FGR morbidity. Thus, early discovery, early diagnosis, early treatment have important clinical and public health implications for FGR.

Metabolomics can be further divided into non-targeted and targeted metabolomics, depending on the purpose of the study. The non-targeted metabolomics is a comprehensive and systematic analysis of endogenous metabolites of organisms, is a non-biased metabolomics analysis, and can discover new biomarkers. Targeted metabolomics is a research analysis directed to a specific class of metabolites. The two have advantages and disadvantages, are often combined for finding and quantifying differential metabolites, and perform deep research and analysis on subsequent metabolic molecular markers, and play an important role in food identification, disease research, animal model verification, biomarker discovery, disease diagnosis, drug development, drug screening, drug evaluation, clinical research, plant metabolism research and microbial metabolism research.

Non-targeted metabonomics

The non-targeted metabolomics is based on limited background knowledge, and the system identification analysis is carried out on the whole organism metabolome to obtain metabolite data and discover differential metabolites. Non-targeted metabonomics mainly comprises the following schemes:

1. sample collection and processing

Common metabonomic analysis samples include plasma, urine, tissue, cells, organelles, and the like. A sufficiently large sample size can reduce errors caused by individual differences in samples. These complex samples contain many other components that may interfere with the results and are critical factors in the success of metabonomics studies. Common sample processing methods include protein precipitation, differential centrifugation and extraction (solid phase extraction, liquid-liquid extraction, supercritical fluid extraction, accelerated solvent extraction), and the like.

2. Experimental analysis

Metabonomics often requires the use of multiple analytical techniques to meet different experimental requirements. Common metabonomic analysis techniques include Nuclear Magnetic Resonance (NMR), liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), capillary electrophoresis-mass spectrometry (CD-MS), HILIC-MS, and the like. The high resolution mass spectrometry technology mainly comprises TOF-MS, FTICR-MS, orbitrap-MS, sector-MS and the like.

3. Data analysis

Data preprocessing: raw data processing is carried out by using tools such as XCMS, MZmine, marker View and the like.

Identification of differential metabolites: common analysis methods include Principal Component Analysis (PCA), partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA), and the like. The data analysis results also required screening for differential metabolites via t-test and variable weight importance ranking (variable importance in projection, VIP) values. It is generally believed that variables that meet at the same time P <0.05, VIP >1.0 are differential metabolites.

Metabolic pathway analysis: common metabonomic pathway databases include databases such as HMDB, KEGG, reactome, bioCyc, metaCyc, which can be used for metabolic pathway and interaction network analysis.

Multiple study analysis: multiple sets of chemical analyses have been the trend of histological findings. Useful databases and tools include the impa la website, ipoap software, metaanalysis website, SAMNetWeb website, pwOMICS, metaMapR, metScape, grinn, WGCNA, mixOmic, diffCorr, qpgraph, huge, and the like.

Targeted metabonomics

Targeted metabolomics is more targeted than non-targeted metabolomics, placing concerns over several or classes of metabolites associated with biological events, such as lipidomics, glycogenomics.

Sample collection and processing

Targeted metabolomics collects samples targeted according to the objects of interest of the targeted metabolomics. For example, when performing lipidomics, solvents with better solubility for lipids are chosen. Quantification of the targeted metabolite is based on a quantitative standard curve of the standard, so that a satisfactory standard needs to be prepared.

Experimental analysis

In the targeting method, natural and isotopically labeled standards facilitate the identification and quantification of metabolites, reducing false positive phenomena. Quantitative metabolomics can be used to establish baseline levels of metabolites in tissues or organisms, for inter-laboratory comparisons, or to define metabolic normals and "perturbation" states. The use of isotopically labeled Internal Standards (IS) can also help explain matrix-induced ionization effects that affect the accuracy of the assay, thereby increasing the sensitivity of the biological response detection assay.

Data analysis

Since targeted metabolomics focuses on a limited class of metabolites, data analysis is simpler and straightforward than non-targeted metabolomics. The methods and databases used are similar to non-targeted metabolomics, but for certain classes of metabolomes there are specific databases, such as the databases of LipidMaps and LipidBank, etc., for carbohydrate and lipid groups.

Because the two metabonomics methods have advantages and disadvantages, the non-targeted metabonomics has no deviation, and the comprehensive system reflects the characteristics of the living body metabonomics, but has poor repeatability and limited linear range; the repeatability and sensitivity of the targeted metabonomics are improved, the metabolite confirmation is simple, the linear range is wide, but a pre-knowledge background is needed, and the method is a biased metabolic analysis method. Therefore, in practical application, the two materials are often combined to play a role together.

In recent years, research on FGR diagnosis, severity evaluation and the like by adopting metabonomics technology is gradually increased, and the FGR diagnosis method has become a research hotspot of clinical and scientific researchers, and has initial application value in early diagnosis of FGR. Metabonomics methods have established several pathways and metabolic processes that may lead to FGR, such as disruption of DNA methylation, cell signaling, neurotransmitter precursors and energy production, and the like. Favretto et al found 22 metabolites that could distinguish gestational age infants (AGA) from FGR newborns, 7 of which were alpha-amino acids, and all compounds were up-regulated in FGR newborns with highest accuracies for tryptophan, phenylalanine and glutamic acid, respectively, to 100% sensitivity and at least 85% specificity. In the study by Sanz-Cortes et al, amino acids were only significant in FGR. Liu et al studied amino acids and acyl carnitine in neonatal blood and found that FGR neonatal homocysteine, methionine, tyrosine, alanine, ornithine and serine levels were lower than the 3 rd percentile of normal infants. One nested case control study conducted by Maitre et al for 36 FGRs and 275 control groups found that the early gestation maternal urine metabolites acetate, tyrosine, formate, trimethylamine, lysine and glycoprotein were associated with risk of developing FGR, underscores the potential of urine metabolic profile as a novel non-invasive biomarker for identifying risk of developing FGR. At present, no metabonomics technology is reported to be applied to FGR auxiliary diagnosis, and if the early biomarkers of FGR can be screened out, the diagnosis of FGR in China is forced. Therefore, the research is based on non-targeted metabonomics to screen urine metabolism difference foreign matters between the pregnant women with the FGR under the birth and the pregnant women with the healthy fetus under the birth, the obtained differential metabolites are applied to clinical diagnosis of the FGR, and urine detection is noninvasive and noninvasive, so that early diagnosis markers of the FGR are screened from the urine, and basis can be accurately and conveniently provided for clinicians to formulate early diagnosis and intervention schemes.

ROC curve (Receiver Operating Characteristic Curve, subject working profile) is a quantitative method that requires experimenters, professional diagnosticians, and predictive workers to make fine decisions or accurate decisions for two conditions or natural states where confusion may or will exist. ROC curves have been widely used in the medical field for clinical diagnosis and treatment, crowd screening, and other studies. Strategies for screening biomarkers using ROC curves mainly include OPLS-DA screening for differential metabolites, selecting important metabolites using minimum absolute shrinkage and selection operator algorithm (Least Absolute Shrinkage and Selection Operator, LASSO) and extreme gradient ascent algorithm (eXtreme Gradient Boosting, XGBoost) and then screening for optimal combinations of metabolites, i.e., candidate biomarkers, using logistic regression models.

Disclosure of Invention

The invention aims to overcome the uncertainty of the existing clinical diagnosis and provides a urine metabolism small molecule combination related to intrauterine growth restriction auxiliary diagnosis and application thereof.

The invention also solves the technical problem of providing a screening method of a urine metabolism small molecule combination for intrauterine growth restriction early diagnosis based on metabonomics.

The invention also solves the technical problem of providing application of the small molecule combination for early diagnosis of urine metabolism with intrauterine growth restriction.

The technical scheme is as follows: in order to solve the technical problems, the inventor utilizes a liquid chromatography-mass spectrometry combined platform to perform non-targeted metabonomics detection on urine of pregnant and lying-in women in early pregnancy, finds and analyzes characteristic differential metabolites related to FGR, screens differential metabolites between FGR and normal control groups, and can be used as a small molecule combination for early diagnosis metabolism of intrauterine growth restriction.

The aim of the invention is realized by the following technical scheme:

an intrauterine growth restriction auxiliary diagnosis metabolism small molecule combination is a combination of metabolism small molecules, namely tigglycine, choline, D-ribose, uric acid, 2-acetyl-3-ethylene-3, 4,5, 6-tetrahydropyridine, dimethyl ketone, propyl cyclopropylamine, cinnamoyl glycine, methionine sulfoxide, L-carnitine, hydroxypropionyl isoleucine, m-aminobenzoic acid, N-desmethyl itracen, uridine, acetaminophen glucuronic acid, o Luo Tiding, sulfuric acid dehydroepiandrosterone, 5-ethyl-4-methylthiazole, 1-3-aminopropyl-4-aminobutyraldehyde, clovoxamine and methyl 2-furoate.

The detection method for detecting the auxiliary diagnosis metabolism small molecule combination is a UPLC-Q actual MS non-target metabonomics detection method. The method is a conventional method, and can entrust a third party detection company to finish detecting the metabolic small molecules in female urine.

The method comprises the following steps:

(1) Liquid phase conditions:

a liquid chromatography column Waters ACQUITY UPLC BEH Amide (2.1 mm. Times.100 mm,1.7 μm) at a column temperature of 25 ℃;

the phase A of liquid chromatography is water phase, contains 25mmol/L ammonium acetate and 25mmol/L ammonia water, the phase B is acetonitrile, and the flow rate is 500 mu L/min;

the instrument gradient is: 0 to 0.5min,95 percent of B; 0.5-7 min, 95-65% B; 7-8 min, 65-40% of B; 8-9 min,40% B; 9-9.1 min, 40-95% of B;9.1 to 12min,95 percent of B;

and (3) sample injection: 3 μL;

(2) Mass spectrometry conditions:

thermo Q Exactive HFX Mass Spectrometry data acquisition is performed by a primary and a secondary mass spectrometer under the control of control software (Xcalibur, thermo), and detailed parameters are as follows: sheath gas flow rate:50Arb,Aux gas flow rate:10Arb,Capillary temperature:320 ℃, full MS resolution:60000, MS/MS resolution:7500,Collision energy:10/30/60in NCE mode,Spray Voltage:3.5kV (positive) or-3.2 kV (negative).

The application of the metabolic small molecule combination in preparing a kit for assisting in diagnosing intrauterine growth limitation is used as a standard substance.

The application of a detection reagent for detecting the metabolic small molecule combination in preparing a kit for assisting in diagnosing intrauterine growth restriction.

A diagnostic kit for aiding in the diagnosis of intrauterine growth restriction, the kit comprising a combination of reagents for detecting said combination of metabolic small molecules. These reagents can be flexibly selected according to the detection method, and are commercially available. The kit provided by the invention contains a reagent for detecting the metabolic small molecule combination in female urine by adopting a UPLC-Q exact MS non-target metabonomics detection method. The kit contains methanol, acetonitrile, ammonium acetate and ammonia water.

Specifically, the technical scheme for solving the problems of the invention comprises the following steps: (1) establishing a unified standard specimen library and database: standard-compliant urine samples were collected with standard procedures (SOP) and the system collected complete demographic and clinical data. (2) non-targeted metabonomic detection of urine: and carrying out non-targeted metabonomics detection on urine of pregnant and lying-in women in early pregnancy by using a liquid chromatography-mass spectrometry platform.

The invention also includes a screening method of a metabolic small molecule combination related to FGR diagnosis, comprising the steps of: the inventor collects urine samples meeting the standard by a Standard Operation Procedure (SOP), the system collects complete demographic data, clinical data and the like, and non-targeted metabonomics detection is carried out on urine of pregnant and lying-in women in early pregnancy to obtain differential metabolites between FGR infants and healthy children according to the specificity, sensitivity and accuracy of the metabolites.

In particular, the experimental method studied mainly comprises the following parts:

the screening method specifically comprises the following steps:

1) Non-targeted LC/MS metabolome sample preparation:

2) Non-targeted LC/MS metabolome condition setting and non-targeted LC/MS metabolome data processing and analysis;

and screening to obtain the metabolic small molecule combination according to the specificity, sensitivity and accuracy of the metabolites.

1. Selection of study objects

The subject group starts to establish a birth queue from 2015 at university of Nanjing medical science, and completes the recruitment of 9898 families in Nanjing and Suzhou by No. 31 in 2019. Inclusion criteria for the subject of the project: (1) single embryo live birth; (2) completing pregnancy follow-up and collecting urine samples in early pregnancy; (3) and (5) completing B ultrasonic monitoring in middle and late pregnancy.

1.1 data collection and follow-up of study subjects

1.1.1 baseline information: collecting the population socioeconomic data (marital, education, address, occupation history, family economic status, age, etc.), height and weight, disease history, family history, reproductive and fertility history, pre-pregnancy health related behaviors and lifestyle (smoking, drinking, exercise, etc.) and pre-pregnancy dietary supplements (folic acid, multivitamin, etc.) of the study subjects by questionnaires;

1.1.2 pregnancy and delivery follow-up: periodic follow-up is carried out in early, middle and late pregnancy, and health related behaviors and life forms (smoking, drinking, physical exercise, and the like), sleep conditions, psychological health conditions (anxiety, depression, and stress), dietary supplements, dietary conditions, and the like are collected through questionnaires; picking early, middle and late stage clinical information (routine prenatal examination, laboratory detection report, etc.) of pregnancy through a hospital electronic information system; acquiring pregnant woman pregnancy related diseases (gestational hypertension, gestational diabetes and the like), neonatal gender, delivery mode, gestational weeks, birth weight, birth ending and the like through an electronic medical record system of a hospital;

1.2 collection of biological samples: collecting urine sample during pregnant woman early pregnancy (10-14 weeks of pregnancy), collecting middle-stage urine into 50ml polypropylene urine cup (without bisphenol), packaging into 5ml polypropylene freezing tube, and freezing at-20deg.C.

2. Non-targeted metabonomics detection of maternal early gestation urine.

2.1 sample pretreatment: (1) preparing 50ml of internal standard stock solution with the concentration of 200 times, and subpackaging into 1ml centrifuge tubes at-80 ℃ for standby; (2) taking an internal standard stock solution, and using methanol: acetonitrile (1:1, v/v) is diluted by 200 times and used as an extracting solution, and precooled at-40 ℃ for standby; (3) thawing the urine sample on ice, and taking 25 μl of the sample to measure osmotic pressure; and taking samples according to osmotic pressure data and diluting the samples to 100 mu l; (4) adding 400 μl of the extract into the sample, mixing, and standing at-40deg.C for 1 hr; (5) centrifuging at 4deg.C for 15min, collecting supernatant, and preserving at-80deg.C (preservation time not longer than 5 days); (6) transferring the sample to a centrifugal refrigerator at 4 ℃ for 60min, and then loading the sample into a machine for LC/MS analysis;

2.2 quality control: monitoring the stability of the instrument and whether the signal is normal or not in real time in the detection process; QC samples were used.

3. Statistical analysis

After original data led out by LC/MS is converted into mzXML format by Proteowizard software, R program package (the kernel is XCMS) is used for carrying out the processes of peak identification, peak extraction, peak alignment, integration and the like, then the obtained product is matched with a BiotreeDB secondary mass spectrum database for carrying out material annotation, and the Cutoff value of algorithm scoring is set to be 0.3. After data normalization, statistical analysis of the non-targeted urine metabolic profile between FGR and control group was performed using MetaboAnalyst (2.0.3) software, the measured data were expressed as mean.+ -. Standard deviation (mean.+ -. SD), and differential metabolites between FPG and healthy children were identified using t-test and Mann-Whitney U test, and statistically significant differential metabolites were screened out at P < 0.05: visualizing inter-group urine metabolic profile differences using principal component analysis (Principal Component Analysis, PCA); differential metabolite enrichment pathway analysis.

The invention has the beneficial effects that:

compared with the prior art, the invention has the following advantages: urine differential metabolites between FGR and normal fetuses are screened out based on non-targeted metabonomics technology. The accuracy, sensitivity and specificity of the disease diagnosis differential metabolites obtained by the non-targeted metabonomics qualitative characteristic phase are higher. Furthermore, the acquisition of urine samples is non-invasive. The metabolic small molecule combination of the invention diagnoses FGR from microscopic metabolite angles, has simple operation, accurate, objective and reliable results, can well distinguish FGR groups from normal groups, provides convenience for clinically accurate diagnosis, can be carried out only by providing urine samples without other tissue samples, greatly improves the possibility and feasibility of clinical application, and provides reference basis for early accurate diagnosis and early intervention scheme of clinicians.

Drawings

FIG. 1, ROC graph for FGR occurrence diagnostic efficiency.

Detailed Description

The invention is further illustrated by the following examples, wherein the procedures not described in detail in the course of the experiment are related procedures known to those skilled in the art, and the reagents used are kit reagents provided by the manufacturer of the apparatus compatible with the detection method and conventional reagents, which are commercially available, and are not specifically described.

Example 1

The inventor starts to establish a birth queue of Nanjing medical university in 2015, finishes home recruitment in Nanjing and Suzhou at 12 months, collects urine samples in early pregnant period (10-14 weeks of pregnancy), collects middle-section urine into a 50ml polypropylene urine cup (without bisphenol) and selects 1459 urine samples meeting the following standards from the urine cup for metabonomics analysis by sorting the sample data, and systematically collects population data, clinical data and the like of the samples.

1. Selection of study samples

(1) Single embryo live birth;

(2) Collecting urine samples in early pregnancy;

(3) And (5) completing B ultrasonic monitoring in middle and late pregnancy.

2. Test materials: chromatographic grade Methanol (Methanol), acetonitrile (Acetonitrile), ammonium acetate (Ammonium acetate), ammonia (Ammonium hydroxide); vanquish ultra performance liquid chromatograph (available from Thermo Fisher Scientific); q exact HFX high resolution mass spectrometer (available from Thermo Fisher Scientific); a Heraeus freesco 17 centrifuge (available from Thermo Fisher Scientific); BSA124S-CW balance (from Sartorius); a clear D24 UV water purifier (available from Merck Millipore); PS-60AL ultrasonic device (available from Shenzhen Lei Debang electronic Co., ltd.).

3. Sample collection and sample data sorting:

in total, 1459 pregnant woman early pregnancy urine samples are used, and the samples have no obvious difference in sampling cycle (P=0.055).

4. Sample preparation

Collecting middle-stage urine into 50ml polypropylene urine cup (without bisphenol), packaging into 5ml polypropylene freezing tube, and freezing at-80deg.C until LC-MS analysis.

5. Metabolite extraction

1) Thawing the sample on clean ice, and mixing;

2) Remove 25 μl urine sample in EP tube and test osmotic pressure;

3) Diluting the urine sample according to the formula: v= 14124.55/(y-8.7545), y: osmotic pressure, V: the volume of the sample/. Mu.L, 100-V/. Mu.L is the volume of the ultra-pure water added, and finally 100. Mu.L of liquid is prepared in an EP tube;

4) Transfer internal standard extraction reagent (methanol: acetonitrile, volume ratio: 1:1) 400. Mu.L in an EP tube, vortexed for 30s, sonicated for 10min;

5) Standing in a refrigerator at-40deg.C for 1 hr;

6) High-speed centrifugation is carried out at 12000rpm and 4 ℃ for 15min;

7) The supernatant was taken in a sample bottle and frozen in a-80 ℃ refrigerator until detection. (the freezing time is less than 7 days).

6. Metabolic group condition settings

Chromatographic conditions: the target compound was chromatographed on a Waters ACQUITY UPLC BEH Amide (2.1 mm. Times.100 mm,1.7 μm) liquid chromatography column using a Vanquish (Thermo Fisher Scientific) ultra high performance liquid chromatograph. Liquid chromatography mobile phase: phase A is aqueous phase containing 25mmol/L ammonium acetate and 25mmol/L ammonia water, and phase B is acetonitrile. Gradient elution is adopted: 0 to 0.5min,95 percent of B; 0.5-7 min, 95-65% B; 7-8 min, 65-40% of B; 8-9 min,40% B; 9-9.1 min, 40-95% of B; 9.1-12 min,95% B. (B refers to mobile phase B, the amount of mobile phase A in each gradient is 100% of the amount of corresponding mobile phase B). Mobile phase flow rate: 0.5mL/min, column temperature: 25 ℃, sample tray temperature: 4 ℃, and the sample injection volume is 3 mu L.

Mass spectrometry conditions: thermo Q Exactive HFX the mass spectrometer is capable of primary and secondary mass spectrometry data acquisition under control of control software (Xcalibur, thermo). The detailed parameters are as follows: sheath gas flow rate:50Arb,Aux gas flow rate:10Arb,Capillary temperature:320 ℃, full MS resolution:60000, MS/MS resolution:7500,Collision energy:10/30/60in NCE mode,Spray Voltage:3.5kV (positive) or-3.2 kV (negative).

7. Metabolome data processing and analysis

After the original data is converted into mzXML format by Proteowizard software, the R program package (the kernel is XCMS) is used for carrying out the processes of peak identification, peak extraction, peak alignment, integration and the like, then the raw data is matched with a BiotreDB (V2.1) secondary mass spectrum database for carrying out material annotation, and the Cutoff value of algorithm scoring is set to be 0.3.

After data normalization, statistical analysis was performed using metanoanalytical tr (2.0.3) software, and analysis methods included principal component analysis (Principal Component Analysis, PCA), T-test, enrichment pathway analysis. The intermediate process of the relevant data processing and statistical analysis is complicated and will not be described in detail.

Analysis revealed that the combination of small metabolic molecules (combination of tigglycine, choline, D-ribose, uric acid, 2-acetyl-3-ethylene-3, 4,5, 6-tetrahydropyridine, p-methyldione, propylcyclopropylamine, cinnamoyl glycine, methionine sulfoxide, l-carnitine, hydroxypropionyl isoleucine, m-aminobenzoic acid, N-desmethylitratriptan, uridine, acetaminophen glucuronic acid, o Luo Tiding, dehydroepiandrosterone sulfate, 5-ethyl-4-methylthiazole, 1-3-aminopropyl-4-aminobutyraldehyde, clovoxamine, methyl 2-furoate) was related to FGR, and that the application of the combination of small metabolic molecules described above to individual groups can well distinguish FGR from normal groups, resulting in fig. 1, with good early-stage auxiliary diagnostic value.

Claims (7)

1. The intrauterine growth restriction auxiliary diagnosis metabolism small molecule combination is characterized in that the metabolism small molecule combination is a combination of metabolism small molecules, namely tiglinide, choline, D-ribose, uric acid, 2-acetyl-3-ethylene-3, 4,5, 6-tetrahydropyridine, p-methyldione, propylcyclopropylamine, cinnamylglycine, methionine sulfoxide, L-carnitine, hydroxypropionyl isoleucine, m-aminobenzoic acid, N-desmethylitramine, uridine, acetaminophen glucuronic acid, o Luo Tiding, dehydroepiandrosterone sulfate, 5-ethyl-4-methylthiazole, 1-3-aminopropyl-4-aminobutyraldehyde, cloxamine and 2-methyl furoate.

2. The detection method for detecting the auxiliary diagnosis of the metabolic small molecule combination according to claim 1, which is characterized in that the detection method of the metabolic small molecule combination is a UPLC-Q actual MS non-target metabonomics detection method.

3. Use of the metabolic small molecule combination of claim 1 in the preparation of a kit for aiding in the diagnosis of intrauterine growth restriction.

4. Use of a detection reagent for detecting the metabolic small molecule combination according to claim 1 for the preparation of a kit for aiding in the diagnosis of intrauterine growth restriction.

5. A kit for aiding in the diagnosis of intrauterine growth restriction, characterized in that it comprises reagents for detecting the combination of metabolic small molecules according to claim 1 in female urine.

6. The auxiliary diagnostic kit according to claim 5, wherein the kit comprises a reagent for detecting the metabolic small molecule combination according to claim 1 in female urine by using a UPLC-Qexact MS non-target metabonomics detection method.

7. The kit according to claim 6, wherein the kit comprises methanol, acetonitrile, ammonium acetate, and aqueous ammonia.