A Metabolomic Study of Polycystic Ovary Syndrome With Insulin Resistance and Its Relationship With TCM Syndrome Types

F

Fujian Maternity and Child Health Hospital

Status

Unknown

Conditions

PCOS

Treatments

Other: all the indicators

Study type

Observational

Funder types

Other

Identifiers

NCT02992093
Dunjingjing

Details and patient eligibility

About

The type-2 diabetes mellitus(T2DM), metabolic syndrome, cardiovascular disease complications induced by polycystic ovary syndrome(PCOS) with insulin resistance(IR), which become serious threat to public health. In this observational study, obese patients with PCOS,nonobese patients with PCOS, PCOS patients with impaired glucose tolerance(IGT), PCOS patients with type-2 diabetes mellitus(T2DM), and healthy volunteers would enrolled into this study, through the Liquid Chromatography-Mass Spectrometry coupled to Mass Spectrometry( LC-MS/MS)and Rapid Resolution Liquid Chromatography(RRLC) and Quadrupole Linear Trap(QTRAP)Mass Spectrometry coupled to Mass Spectrometry (MS/MS)analysis of serum samples collected from PCOS patients and healthy volunteers to screen the biomarker of diagnosis for PCOS with insulin resistance, to explore the correlation between traditional chinese medicine (TCM) syndrome(phlegm, kidney yin deficiency, kidney yang deficiency, qi stagnation and blood stasis,dampness-heat of liver channel)and metabolites of PCOS.

Full description

The selection of research subjects: All the subjects collect from Fujian Maternity and Child Health Hospital. The participants will be divided into five groups:obese with PCOS,nonobese with PCOS,PCOS with IGT,PCOS with T2DM,healthy volunteers.the syndrome type of PCOS will be divided into five types: phlegm, kidney yin deficiency, kidney yang deficiency, liver qi stagnation and blood stasis syndrome, dampness-heat of liver channel. Ethical requirements and subjects' informed consent Before clinical trials begin, the program needs to be approved by the ethics committee to approve and sign the approval.The subjects need fully aware of the clinical trial and are given sufficient time to consider whether they are willing to participate in the trial, and to sign the informed consent form. Indicator test (the cost of all health volunteers are paid by the research) (1)Physical examination: blood pressure, height, body weight,waist circumference,hip circumference,body mass index(BMI),waist-hip ratio(WHR). (2)Endocrine hormones: collect serum from all the subjects,use the enzyme linked immunosorbent assay (ELISA) method to detect the level of serum follicle-stimulating hormone(FSH),luteinizing hormone(LH),prolactin(PRL),estradiol(E2),testosterone(T). Blood lipid: glycerin three vinegar (TG), cholesterol (CHOL) detect by enzymatic method, low density lipoprotein (LDL-C), high density lipoprotein (HDL-C) detect by the turbidity method. Glucose tolerance and insulin release test after glucose loading: all subjects were fasting for 8 to 10 h, check the fasting blood glucose(FBG)and fasting insulin (FINS).The oral glucose tolerance test(OGTT)and insulin release test were performed in the next morning. 5.Study on the characteristics of metabolism (1)Through the sample pretreatment method, analyzes the optimization of the condition of mass spectrometry, according to the requirements of the metabolism to establish the blood sample LC-MS/MS metabolism analysis method.(2)Carry out the analysis of metabolism and data collection.(3)Model building and data analysis(4)Use RRLC and QTRAP type MS/MS with positive and negative ion detection mode with the combination of hyphenated techniques, combined with the method of data statistics,to establish multiple reaction monitoring(MRM) detection, to verify the precision and sensitivity of the method. 6.Statistical methods: All data use statistical product and service solutions18.0(SPSS18.0)software package for statistical analysis. According to the character of clinical trial data (measurement, classification and grade data), select the appropriate statistical analysis method.receiver operating characteristic curve(ROC) analysis of the potential metabolites selected from the targeting metabolic biomarker.

Enrollment

300 estimated patients

Sex

Female

Ages

18 to 40 years old

Volunteers

Accepts Healthy Volunteers

Inclusion criteria

  • The diagnosis of polycystic ovary syndrome (PCOS) according to the Rotterdam consensus criteria recommended by European Society of Human Reproduction and Embryology and American Society for Reproductive Medicine in 2003(2 out of 3):Oligo-and/or anovulation;Clinical and/or biochemical signs of hyperandrogenism;Polycystic ovaries.
  • Diagnostic criteria for insulin resistance: use the HOMA model to evaluate insulin resistance. The HOMA index of insulin resistance (HOMA-IR) = (fasting blood glucose (mmol/L)× fasting insulin (mIU/L) /22.5.
  • voluntary subjects

Exclusion criteria

  • the exclusion of other causes of Kaohsiung hormones, such as congenital adrenal hyperplasia, Cushing syndrome, androgen secreting tumors, and other diseases caused by ovulation disorders, such as hyperprolactinemia, premature ovarian failure, pituitary or hypothalamus closed by etc;
  • exclusion of organic disease or other endocrine diseases;
  • with liver and kidney, cerebral blood vessels, cardiovascular and hematopoietic disorders, such as primary disease, mental patients;
  • patients who had been treated with steroids in nearly three months , such as the birth control pill, and corticosteroids.

Trial design

300 participants in 5 patient groups

obese with PCOS
Description:
all the indicators
Treatment:
Other: all the indicators
nonobese with PCOS
Description:
all the indicators
Treatment:
Other: all the indicators
PCOS with IGT
Description:
all the indicators
Treatment:
Other: all the indicators
PCOS with T2DM
Description:
all the indicators
Treatment:
Other: all the indicators
Healthy volunteers
Description:
all the indicators
Treatment:
Other: all the indicators

Trial contacts and locations

0

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Central trial contact

Jingjing Dun; Jinbang Xu

Data sourced from clinicaltrials.gov

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