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<p>With the rising prevalence of type 2 diabetes (T2D) among children and adolescents, the ability to predict the progression of prediabetes to T2D in youth is imperative, as it significantly affects long-term health and quality of life. A number of biological and social risk factors have been identified in literature. Additionally, a growing body of literature illustrates the use of machine learning techniques to identify those with prediabetes or T2D in adults. A prediction model identifying adults with prediabetes who are most likely to progress to T2D has been suggested; however, to date, no such predictive algorithm in youth with prediabetes, a population with a high rate of spontaneous regression to normoglycemia, has been developed. Machine learning (ML) techniques can be applied to potentially identify novel risk factors for youth-onset T2D progression. The use of ML techniques with longitudinal data would enrich the prediction models to accurately identify children with prediabetes who are at the most risk of developing T2D without intervention. This narrative review summarizes the literature on biological and social risk factors for T2D progression and the use of ML to predict progression to T2D.</p>

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Biological and Social Risk Factors for Predicting Type 2 Diabetes in Youth with Prediabetes: Review of Existing Prediction Models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月19日

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  • Subin Jang,
  • Sisi Ma,
  • Daniel S. Hsia,
  • Kristina Cossen,
  • David Haynes,
  • Megan O. Bensignor

摘要

With the rising prevalence of type 2 diabetes (T2D) among children and adolescents, the ability to predict the progression of prediabetes to T2D in youth is imperative, as it significantly affects long-term health and quality of life. A number of biological and social risk factors have been identified in literature. Additionally, a growing body of literature illustrates the use of machine learning techniques to identify those with prediabetes or T2D in adults. A prediction model identifying adults with prediabetes who are most likely to progress to T2D has been suggested; however, to date, no such predictive algorithm in youth with prediabetes, a population with a high rate of spontaneous regression to normoglycemia, has been developed. Machine learning (ML) techniques can be applied to potentially identify novel risk factors for youth-onset T2D progression. The use of ML techniques with longitudinal data would enrich the prediction models to accurately identify children with prediabetes who are at the most risk of developing T2D without intervention. This narrative review summarizes the literature on biological and social risk factors for T2D progression and the use of ML to predict progression to T2D.