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Effective questionnaire-based prediction models for type 2 diabetes across several ethnicities: a model development

Michail Kokkorakis1,2, Pytrik Folkertsma3,4, Sipko van Dam3,4

  • 1Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.

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Summary

New questionnaire-based models can accurately predict type 2 diabetes prevalence and incidence across diverse ethnicities. These scalable tools offer a promising approach for early disease detection and risk stratification in large populations.

Keywords:
IncidenceMachine learningPopulation screeningPredictionPrevalenceType 2 diabetes

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Area of Science:

  • Diabetes research
  • Epidemiology
  • Public health

Background:

  • Type 2 diabetes (T2D) disproportionately impacts non-White populations.
  • Early detection and prediction are crucial for managing T2D.
  • Large-scale, accessible tools are needed for risk stratification.

Purpose of the Study:

  • To develop and validate questionnaire-based prediction models for T2D prevalence and incidence.
  • To assess model performance across multiple ethnicities.
  • To compare questionnaire-only models with existing clinical tools.

Main Methods:

  • Logistic regression models trained on UK Biobank (White population).
  • Validation in diverse ethnic groups within UK Biobank and externally in Lifelines cohort.
  • Evaluation using Area Under the Receiver Operating Characteristic Curve (AUC).

Main Results:

  • High accuracy for T2D prevalence (AUC=0.901) and incidence (AUC=0.873) in White population.
  • Consistent performance across ethnicities (prevalence AUCs 0.855-0.894, incidence AUCs 0.819-0.883).
  • Models outperformed existing non-laboratory risk tools, improving case reclassification.

Conclusions:

  • Questionnaire-based models provide accurate, scalable prediction of T2D.
  • These models can be implemented widely for population-wide risk stratification.
  • Further validation in diverse global populations is warranted.