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Multiomics-based Prediction Model for GDM

NCT ID: NCT05078697

Last Updated: 2021-10-14

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

100 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-04-01

Study Completion Date

2023-03-01

Brief Summary

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A prospective cohort study was conducted to :1. explore the relative biomarkers of related with the occurence and development; 2. develop a multiotimics prediction model for GDM.

Detailed Description

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Gestational diabetes mellitus (GDM) is one of the most complications during pregnancy and it is associated with many adverse outcomes especially when the pregnant women can't control blood glucose well. With economy develop rapidly, the occurence of GDM is about 15-20%. Recently, the research on GDM has gone into the molecular level, which is beneficial to diagnosis and treatment on GDM. Besides, the cost of hospitalization and treatment for adverse outcomes caused by GDM have caused a certain burden to pregnant women, their families and the whole country. Lifestyle management has the first-line for treatment on GDM, however, the situation of self-management on GDM isn't satisfactory. Therefore, our study aims to explore a model to predict GDM in advance and reduce the adverse outcomes and economy associated with GDM.

The study plan to enroll 60 participants with hisk-risk of GDM and 40 normal participants. They are required sign the inform consent after deciding to participate the research. Furthermore, during early pregnancy (8-13 weeks), middle pregnancy (24-27 3 weeks) and late pregnancy (32-36 weeks), 6-8 weeks after delivery, blood, feces, urine, saliva samples were collected, and continuous blood glucose monitoring system were equipped, human component testing was performed. Follow up was performed to collect the first stool sample of the mother after delivery and faecal samples from the first to the fifth time after birth (the first five times within one week after birth, including hospitalization and discharge) .

Conditions

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GDM

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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early pregnancy women

According to the 2020 high-risk population score model, participants with high risks for GDM and normal health participants will be enrolled into the study.

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

All of following conditions must be met:

* The range of pregnant week is between 8 to 13+6 weeks;
* The range of age is between 20 to 45 years;
* Pregnancy naturally and singleton pregnancy;
* According to the 2020 GDM high-risk population scoring model, those who gets equal to or larger than 80 scores will be assigned to high-risk group and equal to or smaller than 20 scores to low-risk group;

Exclusion Criteria

Meeting one of the following conditions will be excluded:

* Smoking or drinking alcohols;
* Pregnant women who used antibiotics before 1 month enrollment;
* Combined with Chronic hepatitis, nephritis, chronic gastritis, gastric ulcer, chronic colitis, hyperthyroidism, hypothyroidism (except subclinical hypothyroidism) and blood diseases before pregnancy;
* Pregnant women who has a long-term medication history;
* Those who reject signing the inform consent;
* Those who has participated other clinical trail in the same period
Minimum Eligible Age

20 Years

Maximum Eligible Age

45 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers

No

Sponsors

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Peking Union Medical College Hospital

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Liangkun Ma

Role: STUDY_CHAIR

Chinese Academic of Medical Science & Peking Union Medical College Hospital

Locations

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Department of ob gyn , Peking Union Medical College Hospital

Beijing, Beijing Municipality, China

Site Status RECRUITING

Countries

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China

Central Contacts

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Liangkun Ma

Role: CONTACT

[email protected]
13021961166

Suhan Zhang

Role: CONTACT

[email protected]
18611967191

Facility Contacts

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Liangkun Ma

Role: primary

[email protected]
13021961166

Suhan Zhang

Role: backup

[email protected]
18611967191

Other Identifiers

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HS-2297

Identifier Type: -

Identifier Source: org_study_id