Development of a dynamic type 2 diabetes risk prediction tool: a UK Biobank study

TL;DR

This study developed a scalable, smartphone-friendly 10-year type 2 diabetes risk prediction model using UK Biobank data, enabling early identification and prevention without requiring blood tests.

Contribution

It introduces a novel, accessible risk score based on 19 easily obtainable features, outperforming more complex models and suitable for digital deployment.

Findings

Achieved a concordance index of 0.818, indicating high predictive accuracy.

Used only non-invasive features, making the tool suitable for broad, digital health applications.

Showed good calibration and potential for clinical and personal risk assessment.

Abstract

Diabetes affects over 400 million people and is among the leading causes of morbidity worldwide. Identification of high-risk individuals can support early diagnosis and prevention of disease development through lifestyle changes. However, the majority of existing risk scores require information about blood-based factors which are not obtainable outside of the clinic. Here, we aimed to develop an accessible solution that could be deployed digitally and at scale. We developed a predictive 10-year type 2 diabetes risk score using 301 features derived from 472,830 participants in the UK Biobank dataset while excluding any features which are not easily obtainable by a smartphone. Using a data-driven feature selection process, 19 features were included in the final reduced model. A Cox proportional hazards model slightly overperformed a DeepSurv model trained using the same features, achieving a concordance index of 0.818 (95% CI: 0.812-0.823), compared to 0.811 (95% CI: 0.806-0.815). The final model showed good calibration. This tool can be used for clinical screening of individuals at risk of developing type 2 diabetes and to foster patient empowerment by broadening their knowledge of the factors affecting their personal risk.