Metabolomics-Based GDM Prediction Using LC-HRMS Biomarkers
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Solution Overview
Problem
Current methods for diagnosing gestational diabetes mellitus (GDM) are inadequate, as the gold standard oral glucose tolerance test (OGTT) is often too late to prevent birth defects and adverse maternal-fetal outcomes, and patient compliance is poor, leading to delayed intervention.
Innovation Solution
A method using metabolomics to identify biomarkers in serum samples through liquid chromatography-high resolution mass spectrometry (LC-HRMS) and statistical analysis to predict GDM risk, allowing for early detection and differentiation between normal and GDM pregnant women.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the oral glucose tolerance test (OGTT) is used for GDM diagnosis at 24-28 weeks of gestation, then the diagnosis can be made using established clinical criteria, but the diagnosis is too late to prevent birth defects and adverse maternal-fetal outcomes
Solution Approach 1:
The patent applies preliminary action by detecting metabolite markers in serum samples collected during the first or second trimester of pregnancy (before the 24-28 week OGTT window). The metabolite profile analysis predicts GDM risk early, allowing clinical intervention to be initiated before glucose intolerance fully develops, thus preventing birth defects and adverse outcomes while maintaining diagnostic reliability through validated metabolite markers.
2Reliability
If the oral glucose tolerance test (OGTT) is performed at 24-28 weeks of gestation, then the diagnosis can be standardized, but patient compliance with instructions is poor leading to delayed intervention
Solution Approach 1:
The patent replaces the mechanical OGTT procedure (requiring patients to drink glucose solution and undergo multiple blood draws) with a non-invasive serum metabolite profiling approach. By using liquid chromatography-mass spectrometry to analyze endogenous metabolites in routine serum samples, the system eliminates the need for complex patient instructions and multiple visits, significantly improving compliance while maintaining diagnostic standardization through validated metabolic markers.
3Ease of manufacture
If traditional GDM diagnosis methods are used, then the clinical workflow is simple, but the adverse effects of hyperglycemia and related metabolic disorders have already occurred
Solution Approach 1:
The patent implements preliminary action by identifying GDM risk through metabolite profiling in serum samples collected early in pregnancy (first or second trimester), before hyperglycemia and its adverse effects can develop. The system analyzes metabolic markers that predict future glucose intolerance, enabling preventive intervention to avoid the harmful effects of hyperglycemia on maternal and fetal health while maintaining a simple clinical workflow through automated metabolite analysis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the early identification of GDM risk, providing timely clinical intervention and improving maternal-fetal outcomes by accurately distinguishing between normal and GDM pregnant women, thus preventing complications.
Implementation Method 1
The quantitation of the metabolite levels can be accomplished using liquid chromatography-high resolution mass spectrometry (LC-HRMS)
Implementation Method 2
The analysis of metabolites can be untargeted (global) or targeted, typically using mass spectrometry-based techniques
Data Source
AI summary
The present invention relates to a system for predicting gestational diabetes mellitus (GDM) of pregnant individuals, wherein the system comprises an operation module, and the operation model comprises a support vector regression model, and the system is used to predict the plasma glucose levels at 1 hour and/or 2 hours of oral glucose tolerance test (OGTT) by using a support vector regression developed prediction model generated by substituting the concentration of the biomarkers in fasting blood samples of pregnant individuals. The present invention provides biomarkers and biomarker-based diagnostic models for differential diagnosis of gestational diabetes mellitus (GDM), which can be applied to diagnosis or prediction of GDM in early stage and are of great significance to the prevention or treatment of GDM.