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Uric acid is associated with type 2 diabetes: data mining approaches.

Amin Mansoori1,2, Davoud Tanbakuchi1, Zahra Fallahi3

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Diabetology International
|August 5, 2024
PubMed
Summary

Data mining models identified key predictors for type 2 diabetes mellitus (T2D) risk. History of hypertension and dyslipidemia, uric acid, and triglyceride levels were significant indicators.

Keywords:
Biochemical factorsData miningDecision treeTyG indexType 2 diabetesUric acid

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Area of Science:

  • Biomedical research
  • Data science in healthcare
  • Epidemiology

Background:

  • Type 2 diabetes mellitus (T2D) risk is linked to various blood biomarkers.
  • Predictive value of these biomarkers often lacks assessment via data mining.

Purpose of the Study:

  • To develop a predictive model for T2D using data mining algorithms.
  • To assess the predictive value of blood biomarkers and clinical factors for T2D risk.

Main Methods:

  • A cohort study of 9704 participants (aged 35-65) from the MASHAD study (2010-2020).
  • Evaluated serum biochemical factors, lipid profiles, BMI, WC, blood pressure, and age.
  • Employed Logistic Regression (LR) and Decision Tree (DT) for T2D prediction modeling.

Main Results:

  • Diabetic participants showed higher triglyceride, LDL, cholesterol, ALT, direct bilirubin, and uric acid levels (p<0.05).
  • LR model found TG, uric acid, hs-CRP, age, sex, WC, blood pressure, and history of hypertension/dyslipidemia significant for T2D.
  • DT algorithm identified dyslipidemia history as the strongest predictor, followed by age, hypertension history, uric acid, and TG.

Conclusions:

  • Significant associations exist between hypertension/dyslipidemia history, TG, uric acid, hs-CRP, age, WC, and blood pressure with T2D development.
  • LR and DT methods effectively identified key predictors for T2D risk.