A Novel Approach of Determining the Risks for the Development of Hyperinsulinemia in the Children and Adolescent...

Area of Science:

• Pediatrics
• Endocrinology
• Public Health

Background:

• Hyperinsulinemia, characterized by high blood insulin levels, affects children and adolescents.
• Puberty presents significant physiological changes, and prediabetes may lack explicit symptoms.
• Certain health indicators can signal an elevated risk for developing hyperinsulinemia and related conditions.

Purpose of the Study:

• To identify children and adolescents at risk of hyperinsulinemia.
• To analyze risk factors using factor analysis and machine learning algorithms.
• To stratify risk into low, medium, and high categories for targeted interventions.

Main Methods:

• Cross-sectional study involving 674 school-aged children and adolescents (12-17 years).
• Oral Glucose Tolerance Test (OGTT) with insulinemia performed.
• Factor analysis, Radial Basis Function (RBF), and Support Vector Machine (SVM) algorithms applied.

Main Results:

• Statistically significant differences observed between experimental and control groups.
• 17.4% of adolescents were identified as being at risk.
• SVM and factor analysis enabled precise identification and stratification into three risk groups (low, medium, high).

Conclusions:

• Early and accurate identification of at-risk adolescents is crucial for preventing type 2 diabetes and cardiovascular diseases.
• The SVM algorithm provides reliable assessment of risk factor influence.
• Stratified risk assessment aids in timely corrective measures and improves population health outcomes.