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Diabetes Mellitus: Type 2 and Gestational01:22

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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
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Setting Ranges in Potential Biomarkers for Type 2 Diabetes Mellitus Patients Early Detection By Sex-An Approach with

Jorge A Morgan-Benita1, José M Celaya-Padilla1, Huizilopoztli Luna-García1

  • 1Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Zacatecas 98000, Mexico.

Diagnostics (Basel, Switzerland)
|August 10, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning models identified key non-glucose biomarkers, like blood pressure and triglycerides, for early type 2 diabetes mellitus (T2DM) detection. The study highlights sex-specific differences in these markers, improving diagnostic potential and personalized care.

Keywords:
Akaike information criterionbiomarkersmachine learningrecursive feature eliminationtype 2 diabetes

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Area of Science:

  • Endocrinology and Metabolic Diseases
  • Computational Biology and Machine Learning
  • Public Health and Epidemiology

Background:

  • Type 2 diabetes mellitus (T2DM) is a global health challenge requiring early detection to prevent complications.
  • Existing diagnostic tools often rely on glucose levels, necessitating exploration of alternative biomarkers.
  • Understanding sex-specific differences in T2DM risk factors can enhance diagnostic accuracy.

Purpose of the Study:

  • To identify non-glucose-related clinical and anthropometric biomarkers for early T2DM detection using machine learning.
  • To investigate core differences in these biomarkers between males and females for T2DM prediction.
  • To develop and compare machine learning models for robust T2DM risk assessment.

Main Methods:

  • Utilized a dataset of clinical and anthropometric variables from T2DM patients and controls.
  • Applied feature selection techniques including Recursive Feature Elimination (RFE), LASSO, and Genetic Algorithms (GA).
  • Compared five machine learning models (LR, ANN, SVM, KNN, Nearcent) and an ensemble approach for predictive performance.

Main Results:

  • Systolic blood pressure (SBP) and triglycerides were significantly associated with T2DM.
  • Triglycerides, cholesterol, and diastolic blood pressure showed significant sex-specific differences in T2DM patients.
  • The RFE with Random Forest (RF) model achieved the highest accuracy (0.8820) in T2DM prediction.

Conclusions:

  • Machine learning effectively identifies non-glucose biomarkers for early T2DM detection, revealing novel sex-specific profiles.
  • These findings can improve personalized T2DM management by considering anthropometric and clinical differences between sexes.
  • Further validation in diverse populations is recommended to confirm the utility of these biomarkers.