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Diabetes Mellitus: Type 2 and Gestational01:22

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Updated: Jan 7, 2026

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Machine learning-based binary classification of elevated HbA1c (≥6.5 %) for risk assessment.

Dler Hussein Kadir1,2, Azhin Mohammed Khudhur1, Hewir Abdulqadir Khidir1

  • 1Department of Statistics and Informatics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region, Iraq.

Metabolism Open
|January 2, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a predictive model for glycated hemoglobin (HbA1c) using clinical and demographic factors. Age and metabolic indicators like HDL, VitaminD3, VDL, and GPT are key predictors for long-term glycemic management and diabetes risk assessment.

Keywords:
AUCClinical predictorsDiabetes risk assessmentHbA1cLogistic regressionPredictive modeling

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Area of Science:

  • Endocrinology and Metabolism
  • Clinical Diagnostics
  • Biostatistics

Background:

  • Glycated hemoglobin (HbA1c) is a crucial biomarker for assessing long-term glycemic control and diabetes risk.
  • Identifying key clinical and demographic variables can enhance early risk assessment and intervention strategies.
  • Integrating lipid profiles, liver enzymes, and demographic data can improve HbA1c prediction models.

Purpose of the Study:

  • To develop a comprehensive HbA1c prediction model by integrating clinical indicators and demographic characteristics.
  • To identify significant clinical and demographic variables that influence HbA1c levels.
  • To improve early diabetes risk assessment and personalized medical care.

Main Methods:

  • Logistic regression analysis was employed to evaluate 482 cases.
  • A stepwise selection process was utilized to identify the most important predictive factors.
  • The model's predictive ability was assessed using the area under the curve (AUC).

Main Results:

  • The final prediction model demonstrated strong predictive ability with an AUC of 0.797.
  • Significant predictors included age (OR=1.085), glutamate pyruvate transaminase (GPT) (OR=1.011), high-density lipoprotein (HDL) (OR=0.969), VitaminD3 (OR=1.023), and very low-density lipoprotein (VDL) (OR=1.016).
  • Age was the strongest positive predictor, while HDL showed a protective effect.

Conclusions:

  • Age, along with indicators of liver and metabolic function (GPT, HDL, VitaminD3, VDL), significantly impacts HbA1c variability.
  • The integration of common clinical and demographic data into predictive models facilitates early diabetes risk assessment.
  • These findings support individualized medical care and improved patient outcomes in diabetes management.