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MDPI, Medicina, 1(58), p. 9, 2021

DOI: 10.3390/medicina58010009



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Risk Assessment and Determination of Factors That Cause the Development of Hyperinsulinemia in School-Age Adolescents

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Background and Objectives: Hyperinsulinemia and insulin resistance are not synonymous; if the risk of developing insulin resistance in adolescents is monitored, they do not necessarily have hyperinsulinemia. It is considered a condition of pre-diabetes and represents a condition of increased risk of developing DM (diabetes mellitus); it can exist for many years without people having the appropriate symptoms. This study aims to determine the risk of developing hyperinsulinemia at an early age in adolescents by examining which factors are crucial for its occurrence. Materials and Methods: The cross-sectional study lasting from 2019 to 2021 (2 years) was realized at the school children’s department in the Valjevo Health Center, which included a total of 822 respondents (392 male and 430 female) children and adolescents aged 12 to 17. All respondents underwent a regular, systematic examination scheduled for school children. BMI is a criterion according to which respondents are divided into three groups. Results: After summary analyzes of OGTT test respondents and calculated values of HOMA-IR (homeostatic model assessment for insulin resistance), the study showed that a large percentage of respondents, a total of 12.7%, are at risk for hyperinsulinemia. The research described in this paper aimed to use the most popular AI (artificial intelligence) model, ANN (artificial neural network), to show that 13.1% of adolescents are at risk, i.e., the risk is higher by 0.4%, which was shown by statistical tests as a significant difference. Conclusions: It is estimated that a model using three different ANN architectures, based on Taguchi’s orthogonal vector plans, gives more precise and accurate results with much less error. In addition to monitoring changes in each individual’s risk, the risk assessment of the entire monitored group is updated without having to analyze all data.

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