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arXiv · 2104.10108

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

Nikola Dolezalova·Massimo Cairo·Alex Despotovic·Adam T. C. Booth·Angus B. Reed·Davide Morelli·David Plans
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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.

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Nikola Dolezalova, Massimo Cairo, Alex Despotovic, Adam T. C. Booth, Angus B. Reed, Davide Morelli, David Plans. 2021-04-20. Development of a dynamic type 2 diabetes risk prediction tool: a UK Biobank study. https://arxiv.org/abs/2104.10108

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