CN112763570B

CN112763570B - Polycystic ovarian syndrome complicated metabolic syndrome prediction marker and application thereof

Info

Publication number: CN112763570B
Application number: CN202110380286.0A
Authority: CN
Inventors: 乔杰; 赵越; 李蓉; 张春梅; 叶臻泓
Original assignee: Peking University Third Hospital Peking University Third Clinical Medical College
Current assignee: Peking University Third Hospital Peking University Third Clinical Medical College
Priority date: 2021-04-08
Filing date: 2021-04-08
Publication date: 2021-07-27
Anticipated expiration: 2041-04-08
Also published as: CN112763570A

Abstract

The invention provides a group of biomarkers for detecting or diagnosing polycystic ovarian syndrome complicated metabolic syndrome, and further provides a kit for detecting polycystic ovarian syndrome complicated metabolic syndrome and containing the biomarkers and a using method thereof, and the kit can be used for quickly and efficiently diagnosing the onset risk of polycystic ovarian syndrome complicated metabolic syndrome.

Description

Polycystic ovarian syndrome complicated metabolic syndrome prediction marker and application thereof

Technical Field

The invention belongs to the technical field of biological detection, and particularly relates to the field of biomarkers and kits for diagnosis and detection of polycystic ovarian syndrome.

Background

Polycystic ovary syndrome (PCOS) is a complex heterogeneous disease with unknown cause, is the most common reproductive endocrine disease among women of reproductive age, and is the leading cause of anovulatory infertility. Due to inconsistency and ethnicity difference of diagnosis standards of the disease, the morbidity reported by different researches in various countries/regions is greatly different, generally 5% -10%. According to the international widely-used diagnostic standard of Lutedan, the twelve-five national science and technology support plan of the third Hospital of Beijing university from 2007 to 2011 is used for carrying out large-scale hierarchical random sampling investigation on 15924 women with the reproductive age of 19-45 years from 10 provinces in China, so that the PCOS prevalence rate of the women with the reproductive age in China is 5.6%, and about 1500 ten thousand patients exist in the country.

In addition to reproductive disorders in the fertile age, PCOS patients are often associated with long-term, significant metabolic abnormalities, including obesity, insulin resistance, impaired glucose tolerance, dyslipidemia, and the like. The incidence rate of obesity in PCOS women is 30% -60%, and compared with a control population matched with age and weight, patients with PCOS are prone to abdominal obesity, and patients with lean PCOS are prone to abdominal fat accumulation. The proportion of patients with PCOS combined with dyslipidemia is as high as 70%, mainly manifested by a decrease in High Density Lipoprotein (HDL), while the concentrations of Low Density Lipoprotein (LDL), Triglyceride (TG) and cholesterol (T-CHO) are increased. Hyperinsulinemia and Insulin Resistance (IR) are another important characteristic of PCOS patients, the proportion of combined IR in the PCOS patients is as high as 50% -70%, and meanwhile, the prevalence rate of Impaired Glucose Tolerance (IGT) of the PCOS patients is 31% -35%, which is far higher than that of normal people. Based on the above metabolic problems, PCOS patients are at significantly increased risk of developing metabolic syndrome (MetS), while MetS development further increases the risk of the onset of advanced type II diabetes and cardiovascular and cerebrovascular diseases. PCOS is considered a precursor to MetS, and dysglycolipid metabolism is a bridge linking the two.

The metabolic syndrome is a complex group characterized by central obesity, hypertension, hyperlipidemia, and hyperglycemia. Studies report that the incidence and phenotypic characteristics of metabolic syndrome in PCOS patients vary among different ethnic groups. The incidence of MetS incorporation in PCOS women in the United states is as high as 43-46%; in germany, 31% of PCOS women develop MetS, which is most notably characterized by central obesity; women with PCOS in south asia developed MetS rates of 30.6% compared to only 6.34% in the control population; incidence of combined MetS in PCOS women reported in indian areas was 37.5%; the proportion of MetS reported in women in the southwest region in china was then 25.62%, and the incidence of MetS in PCOS women in hong kong was 24.9%, significantly higher than the incidence of MetS in the control population of 3.1%. The results of large-scale random sampling survey of fertile women in 10 provinces of China, which are carried out earlier by the research team, show that the MetS incidence rate of PCOS patients is 18.2%, and in the population over 40 years old, the MetS incidence rate of the PCOS patients is increased to 47.6%, which is obviously higher than that of a control group by 24.4%, which indicates that the risk increasing degree of the PCOS patients to generate MetS is more obvious with the increase of age. Therefore, PCOS is not only a problem of reproductive system in childbearing age, but also a long-term metabolic disorder disease, which affects almost the health of women in life, particularly the long-term quality of life, and is not cured, but the risk of metabolic disease occurrence can be controlled by monitoring and adjusting the state of endocrine-metabolic disorder in the body.

Although the clinical application of the method is more and more important for the metabolic screening of PCOS patients, the clinical manifestations of MetS are various, the global unified diagnosis standard is still lacking, different academic institutions continuously propose and revise the diagnosis standard of MetS, and the method is widely applied to the revised standard of the National Cholesterol Education Program (NCEP) adult group (ATP III 2005) in 2005, meets 3 or more of the following 5 items, can be diagnosed as MetS, and has the following indexes for female Chinese: the waist circumference is more than 80 cm; ② 1.7mmol/L (150mg/dl) of Triglyceride (TG), or has received drug treatment; (iii) high density lipoprotein (HDL-C) <1.3mmol/L (50mg/dl), or has received drug therapy; fourthly, abnormal blood pressure, 130mmHg systolic pressure or 85mmHg diastolic pressure; abnormal fasting blood glucose (FPG), FPG5.6mmol/L (100mg/dl), or has received drug treatment. The clinical routine detection indexes are more, besides waistline and blood pressure, fasting blood glucose and blood lipid (cholesterol, triglyceride, high density lipoprotein and low density lipoprotein) detection is also included, the standard for judging whether MetS occurs is complex, and a rapid and convenient metabolic risk prediction tool is not provided at present.

Disclosure of Invention

In order to solve the problems in the prior art, the invention applies a targeted metabonomics method to carry out the quantitative detection of the full-spectrum amino acid (50) levels of PCOS patients, and screens out an amino acid marker which can detect whether polycystic ovarian syndrome patients have metabolic syndrome. And a risk scoring tool is constructed based on the regression model, so that the clinical application is facilitated.

In a first aspect of the invention, a set of biomarkers and clinical parameters are provided which can be used for the combined diagnosis of polycystic ovarian syndrome complicated with metabolic syndrome, wherein the biomarkers comprise at least 2 specific amino acids, the specific amino acids are alanine and aspartic acid, and the clinical parameters comprise waist circumference and systolic blood pressure.

In one embodiment, the biomarkers of the invention refer to the metabolite components present in a biological sample of a subject, which may be a human or a mammal. The biological sample may be selected from plasma or serum derived from a subject. Preferably, the biological sample is serum.

In a second aspect of the present invention, there is provided a use of a biomarker for the preparation of a kit for diagnosing or detecting PCOS-associated metabolic syndrome, the kit comprising reagents for quantitatively detecting the expression level of the biomarker, the biomarker being alanine and aspartic acid, the kit being used in a method for jointly determining clinical parameters including waist circumference and systolic blood pressure.

In a third aspect of the present invention, there is provided a method for using a PCOS complicated metabolic syndrome diagnostic kit based on quantitative measurement of amino acids, wherein the kit comprises a reagent for quantitatively detecting the expression level of the amino acids, including alanine and aspartic acid, and the test results are input into a logistic regression analysis model for statistical calculation analysis by testing the levels of the amino acids in the plasma or serum of a subject and clinical parameters including waist circumference and systolic blood pressure.

In one embodiment, the method for quantitatively measuring an amino acid is quantitatively detecting the level of a biomarker in the blood or serum of a subject using a mass spectrometer.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:

FIG. 1 ROC curve analysis. 8 AAs signature, joint analysis of 8 amino acids (alanine, valine, leucine, tyrosine, asparagine, glutamic acid, cysteine, glycine), WC, waist circumference, SBP, systolic blood pressure, FPG, fasting plasma glucose, TG, triglyceride level.

FIG. 2 ROC curve analysis. AA + WC + SBP, alanine and asparagine are combined with waist circumference and blood pressure indexes to model and predict the MetS probability of a PCOS patient, WC waist circumference, SBP, systolic blood pressure, FPG, fasting blood glucose, TG and triglyceride level.

Detailed Description

The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.

The experimental procedures, for which specific conditions are not indicated in the examples, are generally carried out under conventional conditions, for example as described in textbooks and experimental guidelines, or as recommended by the manufacturer.

Example 1: screening for differential biomarkers

1. Study object and sample Collection

The study was approved by the ethical committee of the third hospital, beijing university, and female patients who visited the reproductive medicine center of the third hospital, beijing university, from 10 months to 12 months in 2017 were recruited.

1.1 inclusion and exclusion criteria:

inclusion criteria were: the group-entering population is selected according to the 2003 Detddan PCOS diagnosis standard, and the PCOS can be diagnosed when two of the following three conditions are met: infrequent and/or amenorrhea, hyperandrogenism or clinical manifestations of hyperandrogenism (hirsutism, acne, etc.), polycystic changes of the ovary under ultrasound; while excluding other causes of hyperandrogenism.

Exclusion criteria: patients diagnosed with uterine fibroids, endometriosis, reproductive system malformations, and ovarian tumors; patients diagnosed with thyroid disease, diabetes, and cardiovascular disease.

All subjects had no history of taking hormonal drugs within 3 months.

1.2 detection indexes of patients entering the group:

basic indexes are as follows: age, height, weight, blood pressure (systolic pressure, diastolic pressure), waist-hip circumference, etc.;

endocrine metabolism index: drawing blood on day 3-5 of menstruation to detect sex hormone level (prolactin, follicle stimulating hormone FSH, luteinizing hormone LH, estrogen, progesterone, total testosterone T and androstenedione A), sex hormone binding globulin SHBG and anti-muller test tube hormone AMH; fasting glucose, glucose tolerance test (OGTT), fasting insulin, Insulin Tolerance Test (ITT); blood lipids (cholesterol, triglycerides, low density lipoproteins, high density lipoproteins), and the like;

b ultrasonic detection: and carrying out ultrasonic examination on the number and the volume of ovarian follicles (B-ultrasonography through vagina for women who have sexual life experience, B-ultrasonography through abdomen or rectum for women who have asexual life experience) 3-7 days after the menstruation is clear, and determining the antral follicle number (AFC).

2 Mass Spectrometry detection of full-Spectrum amino acid levels

Collecting samples: collecting venous blood 1ml of a patient in an empty stomach state, centrifuging at 3000 rpm multiplied by 10min, and then, reserving supernatant for freezing and storing at-80 ℃ for mass spectrometry; because the amino acid spectrum detection only needs 50 microliters of plasma samples, the residual blood samples of the patients are only required to be reserved for clinical routine examination, and repeated blood sampling is avoided.

The main apparatus is as follows: high performance liquid chromatography-quadrupole ion TRAP tandem mass spectrometer (HPLC/Q-TRAP-MS/MS, LC liquid phase: USA DENAN company; Mass Spectrometry: USA AB company)

The detection method comprises the following steps: the liquid chromatography adopts gradient elution method, wherein the chromatographic column is MSLab-AA-C18, the column temperature is controlled at 50 deg.C, the flow rate is 1 ml/min, and the sample volume is 5 microliter. Mobile phase of the column: a is deionized water (containing 1% formic acid); b is acetonitrile (containing 1% formic acid). Mass spectrum conditions: the ion source is an electrospray ion source, a positive ion detection mode is adopted, and a scanning mode is multi-reaction monitoring. Sample pretreatment: mixing standard sample and blood sample to be tested 50 microliters, adding 50 microliters of protein precipitant, mixing, and centrifuging at 13200 r/min (centrifugation radius of 7.4 cm) for 4 min at 4 ℃. 10. mu.l of the supernatant was mixed with 50. mu.l of a labeling buffer and subjected to flash centrifugation. Adding 20 microliter of derivatization solution, mixing, immediately dissociating, and then performing constant temperature derivatization at 55 ℃ for 15 min. And cooling the derived sample in a refrigerator, uniformly mixing, instantly separating, and taking 50 microliters of the machine for detection.

Quantitative determination of the full-spectrum amino acid indices (50 types): serine, glycine, histidine, threonine, glutamic acid, glutamine, aspartic acid, asparagine, alanine, arginine, proline, cysteine, lysine, methionine, valine, tyrosine, isoleucine, leucine, phenylalanine, tryptophan, phosphoserine, phosphoethanolamine, taurine, hydroxyproline, ethanolamine, citrulline, homocitrulline, ornithine, arginosuccinic acid, sarcosine, β -alanine, carnosine, anserine, α -aminoadipic acid, γ -aminobutyric acid, 3-aminoisobutyric acid, 2-aminon-butyric acid, 5-hydroxylysine, 5-aminopentanoic acid, 6-aminocaproic acid, homocysteine, cystathionine, 5-hydroxytryptophane, kynurenine, 3-methylhistidine, homoarginine, homoproline, A homoserine.

3 screening for amino acid markers useful for accurate diagnosis of PCOS

3.1 determination of amino acids significantly different in the PCOS group

And screening the PCOS patients which meet the grouping standard and have complete indexes, and finally grouping the PCOS patients into 108 cases. According to the ATP III 2005 standard, the occurrence of MetS can be diagnosed in PCOS patients by meeting 3 or more of the following 5 indexes: the waist circumference is more than 80 cm; ② 1.7mmol/L (150mg/dl) of Triglyceride (TG), or has received drug treatment; (iii) high density lipoprotein (HDL-C) <1.3mmol/L (50mg/dl), or has received drug therapy; fourthly, abnormal blood pressure, 130mmHg systolic pressure or 85mmHg diastolic pressure; abnormal Fasting Plasma Glucose (FPG), FPG5.6mmol/L (100 mg/dl). The PCOS combined with the MetS group (PCOS-MS) totaled 59 cases, and the low metabolic risk PCOS group without MetS (PCOS-N) totaled 49 cases, and the basic characteristics and clinical detection index markers of the two groups are shown in Table 1. Age between two groups and a series of endocrine indexes including prolactin, follicle stimulating hormone, estrogen, luteinizing hormone LH, total testosterone, androstenedione, anti-muller test tube hormone AMH and progesterone have no significant difference, PCOS combined MetS group sex hormone binding globulin SHBG is obviously reduced, and free androgen index FAI (FAI = testosterone T multiplied by 100/SHBG) is obviously increased; compared with metabolism related indexes, the two groups have significant differences in fasting blood glucose, fasting insulin level, glucose tolerance, insulin tolerance, triglyceride and high-density cholesterol, and accord with the phenotypic characteristics of metabolic syndrome; in addition, the levels of alanine aminotransferase, aspartate aminotransferase, gamma-glutamyl transpeptidase, uric acid, and C-reactive protein in the PCOS combined MetS group were significantly higher than those in the low metabolic risk PCOS group.

Table 1: PCOS compares clinical detection indexes according to whether MetS generation grouping is merged or not

Note that:

clinical indices english and abbreviations: systolic blood pressure, SBP, diastolic blood pressure, DBP, waist Circumference, WC, watermouth circulation, Prolactin, estrogen, estandiol, Total testosterone, Androstenedione, androsenedione, sex hormone binding globulin, SHBG, sex hormone-binding globulin, free androgen index FAI, Progesterone, prograsterone, fasting blood glucose, FPG, stimulating plasma glucose, fasting insulin, FSI, stimulating hormone insulin, insulin resistance index, HOMA-IR, hormone modules administration of insulin resistance, Total cholesterol, TC, Total cholesterol, low density lipoprotein, LDL-C, low-density protein cholesterol, high density lipoprotein, HDL-C, high-density protein, transaminase, Alanine transaminase, gamma-transferase, gamma-GT, gamma-glutamyl transpeptidase, Uric acid, C-reactive protein, CRP, C-reactive protein.

The statistical analysis method comprises the following steps: data processing and analysis were performed using SPSS 20.0 software. Data conforming to normal distribution are represented by mean values +/-standard deviation, data not conforming to normal distribution are represented by median sum (25% -75% interquartile range difference), difference comparison among groups is respectively represented by t test or nonparametric test (Mann-Whitney U test), p is less than 0.05 to represent that two groups have significant difference, and NS represents that two groups have no significant difference.

The full spectrum amino acid level in the plasma sample of the PCOS patient is quantitatively detected by using a mass spectrometry method, wherein 45 amino acids are in a detection range. Compared with the low metabolic risk PCOS group, the levels of alanine, valine, leucine, tyrosine, glutamic acid, cysteine and amino adipic acid in the PCOS combined MetS group are obviously increased; while asparagine, glycine, 3 aminoisobutyric acid levels were significantly reduced (see table 2).

Table 2: PCOS compares amino acid levels (in. mu. mol/L) according to whether or not to merge MetS occurrence groups

Note that:

amino acid English: alanine, beta-Alanine, Valine, Isoleucine, isoluteine, Leucine, Leucine, Tyrosine, Tyrosine, Phenylalanine, Phenylalanine, Tryptophan, Tryptophan, 5 Hydroxytryptophan, 5-Hydroxytryptophan, Serotonin, Kynurenine, Kynurenine, Glutamic acid, Glutamine acid, Glutamine, Glutamine, Histidine, Histine, 1 Methylhistidine, 3-Methylhistidine, Aspartic acid, Asparagine, Asparaginine, Glycine, Glycine, Arginine, Arginine, Hydroxyarginine, Hydroxyargenine, Proline, Proline, Homoproline, Hydroproline, Hydroxyproline, Hydroxyornithine, Lysine, ornithine, Citrulline, Methionine, cysteine, Homocysteine, Threonine, Serine, Sarcosine, Sarcosine, Taurine, Ethanolamine, Ethanolamine, Phosphoethanolamine, Phosphoethanolamine, alpha-aminoadipic acid, gamma-aminobutyric acid, 2-aminon-butyric acid, 2-aminobutyric acid, 3-aminoisobutyric acid, Cystathionine, Anserine, Anserine, Argininosuccinic acid, Argininosucciic acid.

The statistical analysis method is the same as in Table 1.

3.2 establishing a prediction model for generating MetS by PCOS based on amino acid markers

We selected PCOS in table 2 to incorporate 8 amino acids (alanine, valine, leucine, tyrosine, asparagine, glutamic acid, cysteine, glycine) in the MetS group that were significantly different and relatively abundant, incorporated into logistic regression, selected the entry for the analytical method, and calculated the predicted probability of the subject's occurrence of MetS based on this set of amino acid indices. The comprehensive test P of the Lotist regression analysis model coefficient is less than 0.001, and the model is expressed to be meaningful overall; hosmer and Lemeshow Test (check goodness of fit for model) P =0.429 (>0.05), indicating that the information in the current data has been fully extracted and the model goodness of fit is high. Table 3 the results of the analysis list the amino acids and their parameters incorporated into the model.

Table 3: logistic regression analysis of MetS occurrence of PCOS patient based on 8 amino acid markers

From the above regression results, a risk score logit (p) can be written for each subject to predict whether PCOS occurs or not based on these 8 amino acid levels:

Logit(P)= -7.501 + 0.024* Alanine level -0.020* Valine level+ 0.045* Leucine level-0.027* Tyrosine level-0.057* Asparagine level+0.012* Glutamic acid level+0.082* Cystine level -0.008* Glycinelevel

and the predicted probability of PCOS occurrence of each subject can be calculated according to the following formula:

the area under the characteristic curve (AUC) of the receiver working characteristics (ROC) is further used for judging the prediction ability of the group of amino acids on the occurrence of MetS of PCOS patients, and analysis shows that the area under the ROC curve of a model established by the group of 8 amino acids is 0.892 (0.830-0.953) which is remarkably higher than the commonly used clinical MetS diagnosis indexes of waistline, blood pressure, fasting blood glucose and triglyceride; in addition, the sensitivity of the model is 80.4%, the accuracy is 84.8%, and compared with other clinical indexes, the sensitivity and the accuracy (high waist sensitivity but low accuracy; high fasting blood glucose accuracy but low sensitivity) can be better considered (fig. 1, table 4).

Table 4: ROC curve analysis data

3.3 model for predicting MetS generation of PCOS by combining amino acid and clinical measurement index

We further evaluated whether the combination of the amino acid index and the clinical measurement index can more accurately predict the MetS occurrence of PCOS. Firstly, judging whether a prediction risk model can be established by directly applying main indexes of the existing clinical diagnosis MetS, bringing the waist circumference, the systolic pressure, the diastolic pressure, the fasting blood glucose, the triglyceride and the high-density cholesterol into model variables for multi-factor Logist regression analysis, wherein the comprehensive test P of model coefficients is less than 0.001, and the model is expressed to be generally meaningful; however, Hosmer and Lemeshow Test (check goodness of fit for model) P =0.032 (<0.05), indicating poor model goodness of fit. Therefore, we further combine 8 amino acids determined by 4.3.2 analysis and simple measurement indexes (waist circumference and blood pressure) to construct a simplified and effective model for predicting whether the PCOS generates MetS or not with good fitting degree. A multi-factor Logist regression analysis is applied, the 8 amino acids, the waist circumference, the systolic pressure and the diastolic pressure are included in the model variable analysis, and a stepwise regression method is selected, so that the purpose is to screen independent variables with large effects on dependent variables (whether MetS occurs or not) and reduce the estimation variance as much as possible. The comprehensive test P of the Lotist regression analysis model coefficient is less than 0.001, and the model is expressed to be meaningful overall; the Hosmer and Lemeshow Test (goodness of fit of Test model) P =0.167 (>0.05), indicating that the information in the current data has been fully extracted and that the model goodness of fit is high. The results of the analyses in Table 5 list the amino acids and their parameters that were finally included in the model, which is believed to be effective in predicting MetS in PCOS patients by the Logistic regression model, in combination with waist circumference and systolic blood pressure for 2 amino acids (22).

Table 5: logistic regression analysis of MetS occurrence of PCOS patients

Writing a risk score Logit (P) for predicting whether the PCOS patient has MetS according to the result:

Logit(P)= -22.551 + 0.087*WC + 0.087*SBP + 0.018*Alanine level- 0.057* Asparagine level

and the prediction probability of the PCOS patient to generate MetS can be calculated according to the following formula:

we can assess the accuracy and sensitivity of the prediction model according to the above based on the actual MetS occurrence and the predicted probability of PCOS subjects. The prediction ability of the group of amino acids to the MetS of the PCOS patient is judged by using the area under the characteristic curve (AUC) of the receiver working characteristic (ROC), and analysis shows that the area under the ROC curve of a model established by combining the waist circumference and the systolic blood pressure of the 2 amino acids is 0.913 (0.855-0.970), which is obviously higher than the commonly used clinical MetS diagnosis indexes of waist circumference, blood pressure, fasting blood sugar and triglyceride; in addition, the sensitivity and accuracy of the model are 0.857 and 0.870, and compared with other clinical indexes, the sensitivity and accuracy (high waist sensitivity but low accuracy; high fasting blood glucose but low sensitivity) can be better considered (fig. 2, table 6).

Table 6: ROC curve analysis data

3.4 simple scoring tool for establishing MetS risk of PCOS patient based on Logistic regression model

According to a construction method of a Framingham Heart studio tool for coronary Heart disease risk prediction scoring (a risk function becomes a basis for calculating the coronary Heart disease risk in an adult treatment scheme of the national cholesterol education plan), the model does not use an original continuous variable form, but stratifies risk factors, quantifies and assigns scores to each stratification, and finally evaluates the disease risk of a patient by calculating a total score.

First we group the 4 risk factors in the model of table 5 by layers, wherein the waist, alanine, asparagine are grouped by quartiles, the systolic blood pressure is grouped by tertile, the middle value of each group is selected as the reference value Wij, and for the first group and the last group are semi-closed intervals, we select the values of the 1 st percentile and the 99 th percentile to assist in the calculation, i.e. the reference value Wij of the first group is (1% + Q1)/2, and the reference value Wij of the last group is (Q3+ 99%)/2.

Determining a basic risk division reference value WiREF of each risk factor: for each risk factor, we need to select an appropriate group as the basic risk reference value WiREF, and when a scoring tool is subsequently built, the group value will be scored as 0, the value of the risk factor is higher than the score of WiREF, and the risk is higher when the score is higher, and is conversely lower than the score of WiREF. In the study, the corresponding reference value Wij of waist circumference of less than 80cm, systolic blood pressure of less than 120mmHg, alanine level of less than 342.00 mu mol/L and asparagine level of more than or equal to 54.70 mu mol/L is selected as the basic risk reference value WiREF (shown in red in Table 7) of each risk factor.

Calculating the distance D between each risk factor group and the base risk reference value: the distance D between each group of risk factors and the base risk reference value WiREF is calculated by combining the regression coefficient β i (table 5) estimated by the multi-factor Logistic regression model and the reference value Wij of each group of risk factors, and the calculation formula is D = (Wij-WiREF) × β i.

Setting a constant B corresponding to the score 1 in the scoring tool: the constant of the change of each risk factor for each 1-time score in the scoring tool needs to be set. In this study, the waist circumference was set to be increased by 5cm for 1 point, with the constant B =5 × 0.087= 0.435.

Calculating the score Pointsij corresponding to each classification of the risk factors: on the basis of determining a constant B, calculating the Score corresponding to each classification of the risk factors, wherein the calculation formula is Pointsij = D/B = (Wij-WiREF) × beta i/B, and finally rounding the calculated numerical value to obtain the Score corresponding to the group.

The above score calculation results are shown in table 7.

Table 7: tool for constructing risk score of developing MetS of PCOS patient

Note that: the red color in the table indicates the base risk reference value WiREF for each risk factor.

The correspondence table of the total and risk prediction probabilities is calculated according to table 7: the total score is calculated by adding the predicted scores of each risk factor in table 7, theoretically, when each risk factor takes the lowest value, the lowest value of the total score is 0+0+0+0= 0, and in the same way, the highest value of the total score is 8+9+13+7=37, so that the range of the total score is as follows: 0 to 37 minutes. Then, calculating the risk prediction probability value corresponding to each score according to the equation of the multi-factor logistic regression model, wherein the calculation formula is as follows:

wherein the content of the first and second substances,

constant term + β i Wij + B total score

In the present study, it was determined that,

and the total fraction is about-22.551 +0.087 + 70+0.087 + 105+0.018 + 199.27-0.057 + 91.02+0.435

A correspondence table (table 8) of total score values and risk prediction probabilities is calculated according to the above formula:

table 8: PCOS patient occurrence MetS risk prediction scoring sheet

To this end, we have established a model for predicting MetS in PCOS patients based on four risk factors (waist circumference, systolic blood pressure, alanine, asparagine) (table 3) and further established a simple risk prediction scoring tool (table 8), with increasing risk as the score increases. To verify the accuracy of the scoring tool, the practical application is shown in table 7.

10 cases are randomly selected, total scores are calculated according to clinical indexes of the cases and the values (table 7) of the risk factors in the scoring tool corresponding to the amino acid levels (waist circumference, systolic pressure, alanine and asparagine), risk probabilities corresponding to table 8 are looked up, then the prediction probability (table 5) is calculated according to a multi-factor Logist regression model, and it can be seen that the scoring tool (table 9, red) is similar to the prediction result (table 9, blue) of the Logistic regression model, and the practical application is more visual and convenient.

Table 9: comparison of prediction scoring tool for MetS risk occurrence of PCOS patient and prediction result of multi-factor Logist regression model

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims

1. A kit for diagnosing or detecting PCOS-associated metabolic syndrome, comprising reagents for quantitatively detecting the expression levels of biomarkers, including alanine and aspartic acid, by testing the levels of said amino acids in the plasma or serum of a subject and clinical parameters including waist circumference and systolic blood pressure, inputting the test results into a logistic regression analysis model for statistical computational analysis.

2. The kit for diagnosing or detecting PCOS-associated metabolic syndrome according to claim 1, wherein the quantitative detection method is a quantitative detection of the biomarker level in the blood or serum of the subject using a mass spectrometer.

3. The application of a biomarker in preparing a reagent for diagnosing or detecting PCOS (PCOS-associated metabolic syndrome), wherein the reagent comprises a reagent for quantitatively detecting the expression level of the biomarker, the biomarker comprises alanine and aspartic acid, and the test result is input into a Logist regression analysis model for statistical calculation analysis by testing the level of the amino acid in the blood plasma or blood serum of a test subject and clinical parameters including waist circumference and systolic blood pressure.

4. Use of a biomarker for the manufacture of a reagent for the diagnosis or detection of PCOS-associated metabolic syndrome according to claim 3, wherein the quantitative determination is performed by using a mass spectrometer to quantitatively determine the level of the biomarker in the blood or serum of the subject.