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Establishment of clinical diagnostic models using glucose, lipid, and urinary polypeptides in gestational diabetes

Zhiying Hu1,2, Man Zhang1,2

  • 1Clinical Laboratory Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.

Journal of Clinical Laboratory Analysis
|May 27, 2021
PubMed

Insights

Gestational diabetes mellitus (GDM) poses risks to mothers and infants. This study developed advanced diagnostic models using blood glucose, lipids, and urine polypeptides, improving GDM detection accuracy.

Area of Science:

  • Obstetrics and Gynecology
  • Clinical Diagnostics
  • Biomedical Engineering

Background:

  • Gestational diabetes mellitus (GDM) presents significant short- and long-term health risks for both mothers and infants.
  • Comprehensive analysis of clinical diagnostic markers and urinary polypeptides is crucial for effective GDM management.

Purpose of the Study:

  • To evaluate the diagnostic value of key clinical indexes and urinary polypeptides for GDM.
  • To establish and compare comprehensive diagnostic models for GDM using machine learning techniques.

Main Methods:

  • Retrospective analysis of clinical indexes including serum triglyceride (TRIG), HDL-C, FPG, and HbA1c.
  • Analysis of seven GDM-related urinary polypeptides, including human hemopexin (HEMO).
  • Development of diagnostic models using multiple logistic regression, multilayer perceptron neural network, radial basis function, and discriminant analysis.

Main Results:

  • HbA1c demonstrated the highest diagnostic value among individual clinical indexes (AUC=0.769).
  • Human hemopexin (HEMO) showed the highest diagnostic value among urinary polypeptides (AUC=0.690).
  • The multilayer perceptron neural network model achieved the highest AUC (0.942), outperforming other machine learning models.

Conclusions:

  • A GDM diagnostic model integrating blood glucose, blood lipid, and urine polypeptide indexes offers a robust diagnostic approach.
  • This study supports the application of machine learning and artificial intelligence in developing advanced GDM diagnostic systems.
Abstract

Keywords:
clinical diagnostic modelgestational diabetes mellitusurinary polypeptide

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