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Model for predicting gestational diabetes mellitus by using peripheral blood free DNA

A prediction model, diabetes technology, applied in the direction of microbial determination/inspection, instrumentation, sequence analysis, etc., can solve the problems of large prediction volatility, difficult to propose a stable and reliable prediction model, etc., and achieve the effect of good application prospects.

Active Publication Date: 2019-10-29
GUANGZHOU DARUI BIOTECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the SRY gene is located on the Y chromosome, so it can only be used for disease prediction in male and pregnant women, and the prediction model based only on a single gene is highly volatile, and it is difficult to propose a stable and reliable prediction model

Method used

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  • Model for predicting gestational diabetes mellitus by using peripheral blood free DNA
  • Model for predicting gestational diabetes mellitus by using peripheral blood free DNA
  • Model for predicting gestational diabetes mellitus by using peripheral blood free DNA

Examples

Experimental program
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Effect test

Embodiment 1

[0045] Example 1 Model Method for Predicting Gestational Diabetes Mellitus Based on Peripheral Blood Free DNA

[0046]The method for predicting gestational diabetes mellitus based on free DNA in peripheral blood of the present invention is as follows: compare the sequencing results of free DNA in peripheral blood with the genome sequence map, and then calculate the DNA fragments from the transcription start site region of the gene to be tested in the same sample Quantity, corrected according to the total number of DNA sequences, after uniform correction of the free DNA abundance, using machine learning algorithms, through the optimal combination of different differential genes, to calculate and output the prediction results of gestational diabetes in pregnant women to be tested, which can effectively predict Onset of gestational diabetes.

[0047] Specifically, the method steps are as follows:

[0048] Step 1: Determine where the DNA fragments in the plasma come from on the c...

Embodiment 2

[0078] Embodiment 2 sample detection example

[0079] 1. Experimental sample:

[0080] The training group included 126 samples of gestational diabetes and 378 healthy controls;

[0081] The validation group included 54 samples of gestational diabetes mellitus and 162 healthy controls.

[0082] According to the method operation of embodiment 1. Accuracy, sensitivity and specificity of statistical calculation methods.

[0083] 2. The results show that the method model of the present invention can effectively judge gestational diabetes patients before the early onset in the training group and the verification group (table 4 and figure 2 ).

[0084] Table 4

[0085]

[0086]

[0087] Among them, the calculation result example is as follows:

[0088] Sample 1 (pre-onset sample with confirmed gestational diabetes):

[0089] logit(Y)=0.957+0.565×CC2D2B–1.060×NAT10–1.070×SIPA1–0.620×ZNF565–0.805×ZNF552–0.367×WDR35+0.559×MICALL1–0.653×CTNNB1–0.529×CLOCK–0.674×GIF9LY3T–0 ...

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Abstract

The invention discloses a model for predicting gestational diabetes mellitus by using peripheral blood free DNA. According to research finding of the model, distribution of the peripheral blood free DNA in the region of a gene transcription initiation site can reflect the physiological status of pregnant women and fetuses, after the free DNA abundance is subjected to homogenization correction based on the fact that the serum free DNA abundance in the region of the gene transcription initiation site has the significant difference between gestational diabetes patients and healthy pregnant women,a machine learning algorithm is used, and optimal combination of different differential genes can effectively predict the incidence of gestational diabetes; and thus a screening prediction model forgestational diabetes based on peripheral blood free DNA prediction and an optimized combination of target genes are constructed, the incidence of gestational diabetes can be predicted before the onsetof the clinical symptoms of gestational diabetes, a relatively non-invasive, economical and convenient method for predicting early gestational diabetes is achieved, and good application prospects indeveloping predictive screening products for gestational diabetes are achieved.

Description

technical field [0001] The invention belongs to the technical field of disease detection products. More specifically, it relates to a model for predicting gestational diabetes using cell-free peripheral blood DNA. Background technique [0002] Gestational diabetes mellitus (GDM) is diabetes that occurs during pregnancy, with an incidence of more than 5%, and 80% of the patients are pregnant women with no history of diabetes. Gestational diabetes brings a greater physiological burden to pregnant women. Long-term high blood sugar can easily lead to ketoacidosis, polyhydramnios, premature rupture of membranes, and premature delivery, which can also lead to abnormal embryonic development and even death. The incidence of miscarriage is as high as 15%. ~30%. In addition, gestational diabetes also has great harm to the fetus, and the incidence of fetal malformation, fetal growth restriction, and macrosomia are all significantly increased; after the newborn is born, the incidence ...

Claims

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Application Information

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IPC IPC(8): C12Q1/6883G16B5/00G16B30/00
CPCC12Q1/6883G16B5/00G16B30/00C12Q2600/158
Inventor 韩博炜郭智伟李明吴英松梁志坤欧阳国军
Owner GUANGZHOU DARUI BIOTECH
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