Scholarlink

<p>Various models have been developed for predicting the risk of type 2 diabetes. This study was designed to compare the performance of international and population-specific models in an Iranian population. This cross-sectional analysis examined data from 10,663 participants aged 40–70 years from the Kharameh cohort. The performance of five type 2 diabetes risk prediction models—FINDRISC, ADA, AUSDRISK, the American risk score, and a new logistic regression model was compared using statistical metrics (sensitivity, specificity, AUC) and Decision Curve Analysis to assess clinical utility. As glucose metabolism status worsened from normal to diabetes, mean age, body mass index, waist circumference, and blood pressure increased significantly. The American model showed the highest discriminative ability (AUC = 0.79) and provided the greatest standardized net benefit. The FINDRISC model showed acceptable performance (AUC = 0.72). The ADA and AUSDRISK models performed weaker (AUC = 0.64 and 0.68, respectively). The new logistic model, with an AUC of 0.71, demonstrated high sensitivity (0.82) but low specificity (0.52), making it primarily suitable for high-sensitivity initial screening. The American risk score was the most effective tool, while the lab-free FINDRISC model served as a practical alternative for primary care screening at lower risk thresholds.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Comparative evaluation of international and population-specific risk prediction models for type 2 diabetes: evidence from the kharameh cohort of the PERSIAN study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月3日

查看全文
  • Andishe Hamedi,
  • Mozhgan Seif,
  • Mohammad Hossein Sharifi,
  • Abbas Rezaianzadeh,
  • Hossein Lashkardoost,
  • Jafar Hassanzadeh

摘要

Various models have been developed for predicting the risk of type 2 diabetes. This study was designed to compare the performance of international and population-specific models in an Iranian population. This cross-sectional analysis examined data from 10,663 participants aged 40–70 years from the Kharameh cohort. The performance of five type 2 diabetes risk prediction models—FINDRISC, ADA, AUSDRISK, the American risk score, and a new logistic regression model was compared using statistical metrics (sensitivity, specificity, AUC) and Decision Curve Analysis to assess clinical utility. As glucose metabolism status worsened from normal to diabetes, mean age, body mass index, waist circumference, and blood pressure increased significantly. The American model showed the highest discriminative ability (AUC = 0.79) and provided the greatest standardized net benefit. The FINDRISC model showed acceptable performance (AUC = 0.72). The ADA and AUSDRISK models performed weaker (AUC = 0.64 and 0.68, respectively). The new logistic model, with an AUC of 0.71, demonstrated high sensitivity (0.82) but low specificity (0.52), making it primarily suitable for high-sensitivity initial screening. The American risk score was the most effective tool, while the lab-free FINDRISC model served as a practical alternative for primary care screening at lower risk thresholds.