CN116297799A

CN116297799A - Plasma small molecule metabolic marker for metabolic syndrome and preclinical diagnosis thereof and application thereof

Info

Publication number: CN116297799A
Application number: CN202310109934.8A
Authority: CN
Inventors: 卜军; 钱昆; 陈一凡; 徐伟
Original assignee: Renji Hospital Shanghai Jiaotong University School of Medicine
Current assignee: Renji Hospital Shanghai Jiaotong University School of Medicine
Priority date: 2023-02-14
Filing date: 2023-02-14
Publication date: 2023-06-23

Abstract

A plasma small molecule metabolic biomarker for metabolic syndrome and its preclinical diagnosis, consisting of: pyrimidine, (S) -beta-aminoisobutyric acid, 3-hydroxybutyric acid, L-serine, guanidyl acetic acid, 3-hydroxyisovaleric acid, L-homoserine, L-cysteine, pipecolic acid, hypotaurine, creatinine, phenylacetic acid, erythritol, picolinic acid, taurine, pyroglutamic acid, citraconic acid, malic acid, tyramine, isonicotinic acid, ortho-phosphoethanolamine, L-glutamine, salicylic acid, D-glucose. The invention also provides a kit for detecting the plasma small molecule metabolism biomarker composition, the peak intensity of the plasma small molecule metabolism biomarker is obtained by detection, and then whether each subject is MetS or pre-MetS is predicted by calculating the risk score of whether each subject is MetS or pre-MetS. The invention realizes the high-efficiency and rapid diagnosis of the metabolic syndrome and the preclinical stage thereof.

Description

Plasma small molecule metabolic marker for metabolic syndrome and preclinical diagnosis thereof and application thereof

Technical Field

The invention belongs to the field of biomedicine, and relates to a biomarker, in particular to a plasma micromolecular metabolic biomarker for metabolic syndrome and preclinical diagnosis thereof and application thereof.

Background

In recent years, with the development of social economy and changes in lifestyle and dietary structure of people in China, the incidence of metabolic syndrome (Metabolic Syndrome, metS) and pre-clinical stage (pre Metabolic Syndrome, pre MetS) has been continuously rising, and the prevalence of MetS in men and women has been rising from 25.8% and 18.0% in 2008 to 31.0% and 36.8% in 2010, respectively ^[1] . MetS and pre MetS are considered as independent risk factors for acute cardiovascular and cerebrovascular events as a chronic progressive metabolic disorder ^[2] And is associated with increased incidence of various cancers, cardiovascular diseases and neurodegenerative diseases ^[3-5] . The number of people suffering from MetS and pre MetS and the harm thereof are continuously enlarged, the national health is seriously threatened, the social cost is increased, and the method becomes one of the important public problems in China.

The diagnosis of existing MetS is based on the simultaneous identification of at least three of a group of known risk factors, including obesity, hypertension, hyperglycemia, and dyslipidemia ^[6] . Currently, the diagnostic standard (CDS 2004) suggested by the diabetes society of the China society of medicine and China is widely used in the research of people in China, and 3 or more of the following 4 items are provided for diagnosing as MeS: (1) overweight and/or obesity: the Body Mass Index (BMI) is more than or equal to 25.0kg/m ² The method comprises the steps of carrying out a first treatment on the surface of the (2) Hyperglycemia: fasting blood glucose (FPG) not less than 6.1mmol/L (110 mg/dl) and/or blood glucose (2 hPG) not less than 7.8mmol/L (140 mg/dl) after two hours of meal, and/or diabetic patients with established diagnosis and treatment; (3) hypertension: systolic Blood Pressure (SBP)/Diastolic Blood Pressure (DBP) of 140/90mmHg, and/or to confirm hypertension and treat; (4) dyslipidemia: fasting serum Triglycerides (TG) of 1.7mmol/L (150 mg/dl), and/or fasting blood high density lipoproteins (HDL-C) of < 0.9mmol/L (35 mg/dl) (male) or < 1.0mmol/L (39 mg/dl) (female). The diagnosis of pre MetS refers to any 1-2 of the above 4 items.

The clinical detection indexes related to the guideline proposal are more than 8, and the detection project range is quite complicated. In addition to physical examination of height, weight, blood pressure, etc., various biochemical blood tests (including blood glucose metabolism test items such as FPG and 2hPG, blood lipid metabolism test items such as TG and HDL-C) are also required. Physical examination indexes are often limited by measurement accuracy and manual test techniques, and have high measurement errors. The biochemical test items have different requirements on the fasting/dietary state of the subject, and the combined judgment of whether the MetS and pre MetS occur or not is complex. Therefore, despite the widespread importance of metabolic screening of community populations, high throughput, convenient and efficient MetS and pre MetS diagnostic tool platforms are currently lacking due to the complexity of MetS and pre MetS diagnostics to facilitate accurate medical and management of MetS and pre MetS.

Reference is made to:

[1]LU J,WANG L,LI M,et al.Metabolic Syndrome Among Adults in China:The 2010

China Noncommunicable Disease Surveillance[J].J Clin Endocrinol Metab,2017,

102(2):507-15.

[2]WANG Y,LI J,ZHENG X,et al.Risk Factors Associated With Major CardiovascularEvents 1Year After Acute Myocardial Infarction[J].JAMA Netw Open,2018,1(4):

e181079.

[3]ESPOSITO K,CHIODINI P,COLAO A,et al.Metabolic syndrome and risk of cancer:

a systematic review and meta-analysis[J].Diabetes Care,2012,35(11):2402-11.

[4]CHEN H,ZHENG X,ZONG X,et al.Metabolic syndrome,metabolic comorbid

conditions and risk of early-onset colorectal cancer[J].Gut,2021,70(6):1147-54.

[5]WIECKOWSKA-GACEK A,MIETELSKA-POROWSKA A,WYDRYCH M,et al.Western diet asa trigger of Alzheimer's disease:From metabolic syndrome and systemic

inflammation to neuroinflammation and neurodegeneration[J].Ageing Res Rev,2021,70:101397.

[6] du Guoli, xu Jing, zhang Li et al, epidemiological investigation of the metabolic syndrome of the Kazakhstan-Kazakhstan and comparative study of three diagnostic criteria [ J ]. Chinese general medical science, 2012,15 (24): 2762-4,9.

In recent years, metabonomics based on comprehensive analysis of plasma small molecule metabolites has revolutionized the field of in vitro diagnosis of diseases. Within the system biological framework of genome-transcriptome-proteome-metabolome, metabolome is the stage closest to phenotype in the biodynamic regulatory system, and is the essential feature and material basis of life. MetS and pre MetS patients exhibit different metabolic models than healthy individuals due to disturbances in the metabolic state of the body. There is no report on plasma small molecule metabolic biomarkers for diagnosis of pre MetS patients. The detection methods of plasma small molecule metabolites currently available for MetS diagnosis include Nuclear Magnetic Resonance (NMR) technology and Mass Spectrometry (MS) technology. NMR techniques are limited by their detection sensitivity, and it is difficult to resolve signal to noise overlaps, and to quantify metabolic signals in plasma metabolite peak detection and extraction. Thus, NMR techniques are more difficult to achieve peak extraction of plasma metabolite spectra of MetS and pre MetS. In the MS technology, the most commonly used gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) require complex sample pretreatment steps, including desalting, deproteinizing, derivatizing, concentrating, etc., before metabolic signal detection. At the same time, the reagent consumption and labor costs associated with complex pretreatment steps, longer pretreatment times, and larger required plasma sample volumes limit the throughput of GC/LC-MS detection. Therefore, the GC/LC-MS technique is also more difficult to achieve peak extraction of plasma metabolite profiles of metabolic syndrome.

Matrix assisted laser desorption ionization mass spectrometry (MALDI-MS) is a new technology that has emerged in recent years by mixing an analyte with a matrix material to form co-crystals. Under the irradiation of laser, the object to be detected is ionized due to the laser energy transmitted by the receiving matrix, so that the subsequent mass spectrum analysis is convenient to obtain the spectral peak intensity. Compared with the prior art, the MALDI-MS has the characteristics of high sensitivity, high detection flux, convenient sample pretreatment, high analysis speed, high result tolerance and the like. However, there is no report of peak intensities of plasma metabolite spectra of MetS and pre MetS based on MALDI-MS detection. The present invention has been made in view of the above-mentioned drawbacks of the prior art.

Disclosure of Invention

Aiming at the technical problems in the prior art, the invention provides a plasma micromolecule metabolic biomarker for diagnosing metabolic syndrome and preclinical diagnosis thereof and application thereof, and the plasma micromolecule metabolic biomarker for diagnosing metabolic syndrome and preclinical diagnosis thereof and application thereof aim to solve the technical problems of poor effect in diagnosing metabolic syndrome and preclinical diagnosis thereof in the prior art.

The invention provides a plasma micromolecular metabolic biomarker for metabolic syndrome and preclinical diagnosis thereof, which consists of the following substances: pyrimidine (pyrimide), (S) -beta-aminoisobutyric acid ((S) -beta-Aminoisobutyric acid), 3-hydroxybutyric acid ((R) -3-Hydroxybutyric acid), L-Serine (L-Serine), guanadine acetic acid (Guanidoacetic acid), 3-hydroxyisovaleric acid (3-Hydroxyisovaleric acid), L-Homoserine (L-Homoserine), L-Cysteine (L-Cysteine), piperidine acid (pipcolic acid), hypotaurine (hypatanine), creatinine (Creatinine), phenylacetic acid (Phenylacetic acid), erythritol (erythrol), picolinic acid (Picolinic acid), taurine (taurines), pyroglutamic acid (Pyroglutamic acid), citric acid (citroconic acid), malic acid (Malic acid), tyramine (Tyramine), isonicotinic acid (Isonicotinic acid), ethanolamine orthophosphoric acid (O-Phosphoethanolamine), L-Glutamine (L-glucamine), salicylic acid (salicyclic Glucose), D-Glucose (D-Glucose).

The invention also provides a kit for detecting the plasma small molecular metabolic biomarker, which comprises a reagent for detecting the small molecular metabolic biomarker, wherein the reagent is used for detecting and obtaining the spectrum peak intensity of the plasma small molecular metabolic biomarker in a subject by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and the risk score of whether each subject is MetS or pre-MetS is calculated by the following formula:

log (P) = -2.69 x creatinine spectral peak intensity +1.04 x phenylacetic acid spectral peak intensity +1.72 x erythritol spectral peak intensity + 1.76 x picolinic acid spectral peak intensity +2.55 x taurine spectral peak intensity +0.48 x pyroglutamic acid spectral peak intensity +0.22 x lemon Kang Suanpu peak intensity-0.61 x malic acid spectral peak intensity +1.01 x tyramine spectral peak intensity-0.67 x isonicotinic acid spectral peak intensity +0.23 x ortho-phosphoethanolamine spectral peak intensity +0.14 x l-glutamine spectral peak intensity +0.4 x salicylic acid spectral peak intensity +4.12 x d-glucose spectral peak intensity +0.38 x pyrimidine spectral peak intensity-0.35 x (S) - β -aminoisobutyric acid spectral peak intensity-1.08 x 3-hydroxybutyric acid spectral peak intensity +2.59 x serine spectral peak intensity + 0.40.35 x serine spectral peak intensity-0.14 x L-glutamine spectral peak intensity +0.4 x salicylic acid spectral peak intensity-0.35 x salicylic acid spectral peak intensity + 0.40-0.35 x serine spectral peak intensity-0.35 x 1.35 x 6 x serine spectral peak intensity-0.0.6 x-0.35 x cysteine spectral peak intensity-0.0.35 x 0.33 x 0.14 x-0 x linear acid spectral peak intensity;

wherein, the sex is 1 for male and 0 for female;

wherein the correction coefficient of age is 0.038, and the correction coefficient of sex is 0.066;

predicted probability of whether each subject is MetS or pre-MetS:

wherein, when Pro is less than or equal to 0.5, the test subject is metabolic healthy people;

when Pro is more than 0.5 and less than or equal to 0.8, indicating that the subjects are pre-MetS groups;

pro > 0.8, indicating that the subject is a MetS population.

In one embodiment, the present invention comprises plasma metabolism fingerprint of a metabolite component present in a biological sample of a subject. The subject is human, the biological sample is derived from the plasma of the subject, and the reagent comprises deionized water and 1ng/nL of iron nanometer matrix solution.

The invention also provides application of the spectral peak intensity of the plasma small molecule metabolism biomarker obtained by using the matrix-assisted laser desorption ionization time-of-flight mass spectrometer to preparation of a product for predicting or detecting the metabolism syndrome and the preclinical stage thereof, wherein the plasma small molecule metabolism biomarker is as follows: pyrimidine, (S) - β -aminoisobutyric acid, 3-hydroxybutyric acid, L-serine, guanacetic acid, 3-hydroxyisovaleric acid, L-homoserine, L-cysteine, pipecolic acid, hypotaurine, creatinine, phenylacetic acid, erythritol, picolinic acid, taurine, pyroglutamic acid, citraconic acid, malic acid, tyramine, isonicotinic acid, ortho-phosphoethanolamine, L-glutamine, salicylic acid, D-glucose, and the following formulas were used to calculate a risk score for each subject as MetS or pre-MetS:

wherein, the sex is 1 for male and 0 for female;

predicted probability of whether each subject is MetS or pre-MetS:

pro > 0.8, indicating that the subject is a MetS population.

The invention also provides application of the plasma small molecule metabolic biomarker in preparing a kit for diagnosing or detecting metabolic syndrome and preclinical stage thereof, and the application method of the kit comprises the steps of diagnosing or detecting whether the test body has metabolic syndrome or has the preclinical stage of the metabolic syndrome by testing the spectrum peak intensity of the plasma small molecule metabolic biomarker in the plasma of the test body and correcting clinical indexes such as age, sex and the like.

In one embodiment, the kit is used by detecting a characteristic plasma small molecule metabolic biomarker in the subject's plasma using the MALDI-MS method.

The invention detects a group of plasma micromolecular metabolic biomarkers capable of specifically representing the metabolic syndrome and the preclinical stage thereof in a large-scale natural crowd queue by using a MALDI-MS method, and constructs a diagnosis tool based on a minimum absolute value convergence and selection operator model (LASSO) of machine learning, thereby being convenient for clinical popularization and application.

The invention provides a group of plasma micromolecular metabolic biomarkers for metabolic syndrome and preclinical diagnosis thereof, and further provides a kit for metabolic syndrome and preclinical diagnosis thereof and an application method thereof, wherein the kit comprises the group of plasma micromolecular metabolic biomarkers.

According to the invention, accurate spectral peak intensity measurement of peripheral blood metabolism micromolecules is realized through matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS), and the high-efficiency and rapid diagnosis of metabolic syndrome and the preclinical stage thereof is realized by combining with machine learning algorithm model construction, so that a new way is provided for evaluating the real change of human metabolic disorder, and the health management of community groups is facilitated.

Drawings

FIG. 1 is a peak profile of characteristic plasma small molecule metabolites for MetS patients, pre MetS patients and healthy controls based on MALDI-MS detection;

FIG. 2 is a plot of signals of consistency of 13554 plasma samples based on MALDI-MS detection;

FIG. 3 is a differential metabolite small molecule screening assay of this example;

FIG. 4 is a graph showing the diagnostic efficacy curve analysis of the present embodiment;

FIG. 5 is a ROC curve analysis of plasma small molecule metabolites diagnosing metabolic syndrome and its early stages;

FIG. 6 is a diagnostic index analysis of plasma small molecule metabolites for diagnosing metabolic syndrome and its early stages.

Detailed Description

The following description of the preferred embodiments of the present invention is provided in conjunction with the accompanying drawings, which are intended to illustrate and explain, rather than limit, the invention.

The experimental methods for which specific conditions are not specified in the examples are generally in accordance with conventional conditions, such as those described in textbooks and experimental guidelines, or in accordance with recommended conditions provided by the manufacturer.

Example 1: extraction of plasma small molecule metabolites characteristic of metabolic syndrome

1. Study and sample collection

The study was approved by the institutional ethical committee of the Shanghai university of traffic university, affiliated with the Shanghai Hospital, recruiting subjects from 1 month in 2019 to 10 months in 2020 to the Shanghai Pudong natural population cohort.

1.1 inclusion and exclusion criteria:

inclusion criteria: according to the diagnosis standard (CDS 2004) recommended by the diabetes society of the China medical society, three or more of the following four terms are met and can be diagnosed as MeS: (1) overweight and/or obesity: the Body Mass Index (BMI) is more than or equal to 25.0kg/m ² The method comprises the steps of carrying out a first treatment on the surface of the (2) Hyperglycemia: fasting blood glucose (FPG) not less than 6.1mmol/L (110 mg/dl) and/or blood glucose (2 hPG) not less than 7.8mmol/L (140 mg/dl) after two hours of meal, and/or diabetic patients with established diagnosis and treatment; (3) hypertension: systolic Blood Pressure (SBP)/Diastolic Blood Pressure (DBP) of 140/90mmHg, and/or to confirm hypertension and treat; (4) dyslipidemia: fasting serum Triglycerides (TG) of 1.7mmol/L (150 mg/dl), and/or fasting blood high density lipoproteins (HDL-C) of < 0.9mmol/L (35 mg/dl) (male) or < 1.0mmol/L (39 mg/dl) (female). While compliance with one or both of the four above may be diagnosed as pre MetS.

Exclusion criteria: patients with acute and infectious clinical symptoms three weeks prior to sampling, including but not limited to fever, headache, cough, malaise, sore throat, loss of smell, runny nose, abdominal pain, diarrhea, and the like.

According to inclusion and exclusion criteria, 3504 patients with MetS, 7776 pre MetS patients, 2274 healthy control subjects were finally included.

1.2 group-entering patient detection index:

basic indexes are as follows: age, sex, height, weight, body Mass Index (BMI), blood pressure (systolic, diastolic);

biochemical indexes: fasting blood glucose, two hours postprandial blood glucose, fasting serum triglyceride, fasting blood high density lipoprotein.

1.3 plasma sample collection:

peripheral venous blood of the patient was collected in a fasting state at 1ml, centrifuged at 4000rpm X10 min, and the supernatant was kept at-80℃for subsequent MALDI-MS measurement. Only 100nL of plasma sample is needed for MALDI-MS detection.

Example 2: MALDI-MS detection of plasma small molecule metabolite spectrum peak

2.1 main instrument: autoflex Speed MALDI TOF/TOF matrix assisted laser Desorption ionization time-of-flight mass spectrometer (Bruce Germany)

2.2 molecular biochemistry reagent: deionized water and iron matrix material

2.3 plasma sample treatment:

100nL of the plasma sample to be tested was diluted 10:1 with deionized water, and 500nL of the diluted plasma sample was taken on a MALDI-MS target plate and dried at room temperature.

2.4 preparation of iron nanoparticle matrix:

1ng/nL of iron nanomatrix solution was prepared, 500nL of iron nanomatrix solution was taken on a plasma sample of MALDI-MS target plate and dried at room temperature.

2.5MALDI-MS detection:

in a positive ion mode, performing mass spectrometry detection on the iron nanoparticle matrix on the target plate by using MALDI-MS; and performing result preprocessing by using Malab software, wherein the result preprocessing comprises data resampling, spectral line smoothing, baseline correction, spectral peak matching and missing value filling, and finally obtaining the spectral peak intensity (intensity) of the full-spectrum small molecule metabolite.

After comprehensive comparison of the secondary spectrum chart library and the HMDB database (Human Metabolome Database), 303 full spectrum plasma small molecular metabolites with mass-to-charge ratio (m/z) smaller than 300 are obtained, characteristic full spectrum plasma small molecular metabolite spectrum peak intensity charts (figure 1) of MetS patients, pre MetS patients and healthy control persons are drawn, and characteristic signal charts (figure 2) of 13554 plasma samples are obtained.

2.6 screening for characteristic plasma small molecule metabolites for MetS and pre MetS diagnostics

To screen for characteristic plasma small molecule metabolites for MetS and pre MetS diagnostics, data processing and analysis was performed using the R4.0.5 language. Following normal distribution of the test data, differentially expressed metabolites between three states, healthy Control (HCs), pre MetS and MetS were assessed by Kruskal-Wallis rank sum test function in the R4.0.5 language.

The level of significance was set at p <0.05. At the same time, multiple comparisons of three sets of data were performed using Bonferroni correction in the R4.0.5 language, filtering out 24 plasma small molecule metabolites with significant expression differences (fig. 3):

pyrimidine (pyrimide), (S) -beta-aminoisobutyric acid (S) -beta-Aminoisobutyric acid), 3-hydroxybutyric acid (R) -3-Hydroxybutyric acid), L-Serine (L-Serine), guanadine acetic acid (Guanidoacetic acid), 3-hydroxyisovaleric acid (3-Hydroxyisovaleric acid), L-Homoserine (L-Homoserine), L-Cysteine (L-Cysteine), piperidine acid (pipcolic acid), hypotaurine (hypatanine), creatinine (Creatinine), phenylacetic acid (Phenylacetic acid), erythritol (erythrol), picolinic acid (Picolinic acid), taurine (taurines), pyroglutamic acid (Pyroglutamic acid), citric acid (cic acid), malic acid (Malic acid), tyramine (Tyramine), isonicotinic acid (Isonicotinic acid), ethanolamine orthophosphoric acid (O-Phosphoethanolamine), L-Glutamine (L-glucamine), salicylic acid (salicyclic Glucose) and D-Glucose (D-1) (table 1).

TABLE 1 differential expression of characteristic plasma small molecule metabolites between MetS group, pre MetS group and healthy controls

2.7 establishing a diagnostic MetS model based on characteristic plasma Small molecule metabolites

MetS patients, pre MetS patients and healthy controls which meet the group-entering standard and have complete indexes are screened, and the group-entering is matched according to the ratio of 1:1 for modeling.

Wherein, in the MetS and healthy control distinguishing model (4548 cases of the same group), 2274 cases of healthy control and 2274 cases of MetS patients are included; pre MetS and healthy control discrimination models (4548 cases in the same group), 2274 cases in the group healthy control, 2274 cases in pre MetS patients; pre MetS and MetS differentiation models (group 7008 cases together), group MetS patients 3504 cases, pre MetS patients 3504 cases.

Subject operating characteristic curve (ROC) area under test (AUC) statistical efficacy analysis on sample volumes using a double-sided z-test, in AUC ₀ =0.75 as reference, AUC ₁ The results, estimated at=0.80, showed that the diagnostic efficacy reached 100% for sample sizes of 4548 and 7008 (fig. 4).

Diagnostic models of MetS and pre MetS are further established, respectively. In each model, subjects were divided into training and test sets at a 7:3 ratio, respectively. Wherein, in the distinguishing model of MetS and healthy control, the training collection was 3184 subjects and the test collection was 1364 subjects. In the pre MetS and healthy control differential model, the training collection was 3184 subjects and the test collection was 1364 subjects. In the pre MetS and MetS differential model, training and test collections were carried over into 4906 subjects and test collections were carried over into 2102 subjects.

The spectral peak intensities of the set of characteristic plasma small molecule metabolites were machine-learned using a minimum absolute convergence and selection operator provided by the glrnet package in R4.0.5 and a generalized linear algorithm of elastic network regularization (Generalized Linear Models via Lasso and Elastic-Net Regularization, glrnet) in combination with 8-fold cross validation (cross validation), to determine the predictive probability of each subject developing MetS and pre MetS, and to correct for age-sex. The analysis results in Table 2 show the metabolites and parameters thereof included in the model after gender correction for age, wherein the age correction factor was 0.038 and the gender correction factor was 0.066.

TABLE 2 Glmnet analysis to construct MetS diagnostic model based on the spectral peak intensities of 24 plasma Metabolic small molecules (model parameters Lambda approximately 0.0003)

Metabolites and methods of use	English name	HMDB library numbering	Model coefficient (beta)	OR value
					Creatinine	Creatinine	HMDB0000562	-2.69	0.07
Phenylacetic acid	Phenylacetic acid	HMDB0000209	1.04	2.82
					Erythritol	Erythritol	HMDB0002994	1.72	5.60
Picolinic acid	Picolinic acid	HMDB0002243	-1.76	0.17
					Taurine	Taurine	HMDB0000251	-2.55	0.08
Pyroglutamic acid	Pyroglutamic acid	HMDB0000267	0.48	1.62
					Citric acid	Citraconic acid	HMDB0000634	0.22	1.25
Malic acid	Malic acid	HMDB0000156	-0.61	0.54
					Tyramine	Tyramine	HMDB0000306	1.01	2.73
Isonicotinic acid	Isonicotinic acid	HMDB0060665	-0.67	0.51
					O-phosphoethanolamine	O-Phosphoethanolamine	HMDB0000224	0.23	1.26
L-glutamine	L-Glutamine	HMDB0000641	0.14	1.14
					Salicylic acid	Salicyluric acid	HMDB0000840	0.40	1.49
D-glucose	D-Glucose	HMDB0000122	4.12	61.71
					Pyrimidine	Pyrimidine	HMDB0003361	0.38	1.46
(S) -beta-aminoisobutyric acid	(S)-beta-Aminoisobutyric acid	HMDB0002166	-0.35	0.71
					3-hydroxybutyric acid	(R)-3-Hydroxybutyric acid	HMDB0000011	-1.08	0.34
L-serine	L-Serine	HMDB0000187	2.59	13.37
					Guanidine acetic acid	Guanidoacetic acid	HMDB0000128	0.44	1.56
3-hydroxyisovaleric acid	3-Hydroxyisovaleric acid	HMDB0000754	-1.75	0.17
					L-homoserine	L-Homoserine	HMDB0000719	-0.05	0.95
L-cysteine	L-Cysteine	HMDB0000574	-1.26	0.28
					Piperidine acid	Pipecolic acid	HMDB0000070	-0.17	0.85
Sulfenate acid	Hypotaurine	HMDB0000965	1.21	3.34

The mathematical model of the machine learning iteration is as follows by using the glmnet () function built in the glrnet package of R4.0.5 language, and the risk score of whether each subject is MetS or pre-MetS can be calculated by the spectral peak intensities and sex ages of 24 plasma metabolism small molecules in human body as follows:

wherein, the sex is 1 for male and 0 for female;

the predicted probability of whether each subject is MetS or pre-MetS is:

the prediction probability (Pro) of each subject is iteratively and sequentially compared by using a prediction () function built in the glrnet package of R4.0.5 language, and the threshold range division condition of the prediction probability can be automatically output:

pro is less than or equal to 0.5, which indicates that the subject is metabolic healthy people;

pro > 0.8, indicating that the subject is a MetS population.

On this basis AUC was used to evaluate the predictive ability of subjects to develop MetS and pre MetS based on the peak intensities of characteristic plasma small molecule metabolite profiles (figure 5).

The evaluation results based on AUC show that effective diagnostic models of MetS and pre MetS can be established by using the spectral peak intensities of the characteristic plasma small molecule metabolites.

Example 3: the model was evaluated as follows:

AUC, which distinguishes healthy controls from pre MetS, was 0.867 in the training set (sample size = 3184 people), 95% confidence interval (0.854-0.881), 0.864 in the test set (sample size = 1364 people), 95% confidence interval (0.843-0.885). Test results for the dilong's test were p=0.813, showing no significant differences in AUC of the model between the training set and the test set.

The AUC, which distinguishes pre MetS from MetS, was 0.849 in the training set (sample size=4906 person), was (0.838-0.860) in the 95% confidence interval, and was 0.835 in the test set (sample size=2102 person), was (0.817-0.853) in the 95% confidence interval. Test results for the dilong's test were p=0.195, showing no significant differences in AUC of the model between the training and test sets.

AUC, which distinguishes healthy controls from MetS, was 0.891 in the training set, 0.880-0.902 in the 95% confidence interval, 0.886 in the test set, and 0.868-0.903 in the 95% confidence interval. The test result of the dilong "s test was p=0.605, showing that the AUC of the model was not significantly different between the training set and the test set.

The results show that the MetS and pre MetS diagnostic models constructed by using the spectral peak intensities of the characteristic plasma small molecular metabolites have good diagnostic efficiency and strong generalization capability, and can be further popularized and applied. Meanwhile, the model can give consideration to better sensitivity, specificity and accuracy, and shows better positive prediction rate, negative rate prediction rate and F1 value in both training set and verification set (figure 6).

It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. A plasma small molecule metabolic biomarker for metabolic syndrome and its preclinical diagnosis, characterized by consisting of: pyrimidine, (S) -beta-aminoisobutyric acid, 3-hydroxybutyric acid, L-serine, guanidyl acetic acid, 3-hydroxyisovaleric acid, L-homoserine, L-cysteine, pipecolic acid, hypotaurine, creatinine, phenylacetic acid, erythritol, picolinic acid, taurine, pyroglutamic acid, citraconic acid, malic acid, tyramine, isonicotinic acid, ortho-phosphoethanolamine, L-glutamine, salicylic acid, D-glucose.

2. A kit for detecting the plasma small molecule metabolic biomarker of claim 1, characterized in that: the reagent for detecting the micromolecular metabolic biomarker comprises a reagent for detecting and obtaining the spectrum peak intensity of the plasma micromolecular metabolic biomarker in a subject by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and calculating whether each subject is MetS or pre-MetS according to the following formula:

wherein, the sex is 1 for male and 0 for female;

predicted probability of whether each subject is MetS or pre-MetS:

pro > 0.8, indicating that the subject is a MetS population.

3. A kit for detecting a plasma small molecule metabolic biomarker according to claim 2, characterized in that: the subject is human, the biological sample is derived from the plasma of the subject, and the reagent comprises deionized water and 1ng/nL of iron nanometer matrix solution.

4. Use of a plasma small molecule metabolic biomarker according to claim 1 in the manufacture of a kit for diagnosing or detecting metabolic syndrome and its preclinical stages.

5. The application of the peak intensity of the plasma small molecule metabolism biomarker obtained by detecting by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer in the preparation of products for predicting or detecting the metabolism syndrome and the preclinical stage thereof is that the plasma small molecule metabolism biomarker is: pyrimidine, (S) - β -aminoisobutyric acid, 3-hydroxybutyric acid, L-serine, guanacetic acid, 3-hydroxyisovaleric acid, L-homoserine, L-cysteine, pipecolic acid, hypotaurine, creatinine, phenylacetic acid, erythritol, picolinic acid, taurine, pyroglutamic acid, citraconic acid, malic acid, tyramine, isonicotinic acid, ortho-phosphoethanolamine, L-glutamine, salicylic acid, D-glucose, and the following formulas were used to calculate a risk score for each subject as MetS or pre-MetS:

wherein, the sex is 1 for male and 0 for female;

predicted probability of whether each subject is MetS or pre-MetS:

pro > 0.8, indicating that the subject is a MetS population.