Updating a clinical prediction model for identifying monogenic diabetes to include both clinical features and biomarkers
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posted on 2025-10-14, 17:03 authored by Julieanne Knupp, Pedro Cardoso, Katherine G. Young, Timothy J. McDonald, Kashyap A. Patel, Kevin Colclough, Ewan R. Pearson, Angus G. Jones, Sophie Jones, Shivani Misra, Andrew T. Hattersley, Trevelyan J. McKinley, Beverley M. Shields<p dir="ltr">Objective </p><p dir="ltr">Selecting appropriate individuals for monogenic diabetes genetic testing is challenging. We aimed to develop a new probability calculator, integrating clinical features and biomarkers, to aid identification of monogenic diabetes. </p><p dir="ltr">Research Design and Methods </p><p dir="ltr">We developed two prediction models (for early-insulin-treated, proxy for type 1 diabetes; and not-early-insulin-treated patients, proxy for type 2 diabetes) using a Bayesian recalibration mixture model approach. We used case-control data (monogenic diabetes=594, non-monogenic diabetes diabetes=597) for initial model development (clinical features only) and recalibrated to population-data (UNITED study, n=1,299) including biomarkers (C-peptide and islet-autoantibodies). We externally validated the calculator in an independent population-based cohort (n=1,025). </p><p dir="ltr">Results</p><p dir="ltr">For early-insulin-treated individuals, the model incorporating biomarkers improved discrimination over using clinical features only (ROCAUC 0.98 [95%CrI 0.95–0.98] vs. 0.80 [95%CrI 0.71–0.82], p<0.001) or biomarkers alone (ROC AUC 0.96 [95% CI 0.95–0.97]). For not-early-insulin-treated participants, the calculator showed good discrimination (ROCAUC: 0.86 [95%CrI 0.85–0.88]). Both models calibrated well and showed good discrimination in external validation (0.98 and 0.92 for early- and not-early-insulin-treated individuals, respectively). Using a ≥5% probability threshold to guide testing results in positive test rates for monogenic diabetes of 16–19%. </p><p dir="ltr">Conclusions</p><p dir="ltr">We developed an updated monogenic diabetes probability calculator that integrates both clinical features and biomarkers, providing greater discrimination than using clinical features or biomarkers alone and providing appropriate measures for selecting individuals for monogenic diabetes diagnostic testing. This is now available as an online calculator and has immediate clinical utility for White European individuals diagnosed with diabetes ≤35 years. </p><p><br></p>
Funding
For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. This project was funded by Diabetes UK (reference 21/0006328). This article is based in part on data from the CPRD obtained under license from the UK Medicines and Healthcare products Regulatory Agency. CPRD data are provided by individuals and collected by the UK National Health Service (NHS) as part of their care and support. Approval for CPRD data access and the study protocol was granted by the CPRD Independent Scientific Advisory Committee (eRAP protocol number: 23_003615). The MY DIABETES study was funded by the NIHR BRC, the Diabetes Research & Wellness Foundation (Sutherland Earl Fellowship awarded to S.M. in 2013), Imperial College Healthcare NHS Trust Hospital Charity (2012) and Wellcome Trust (grant 223024/Z/21/Z). The views expressed are those of the author(s) and not necessarily those of NIHR or the Department of Health and Social Care. P.C. and T.J.M. (McKinley) were funded by Research England’s Expanding Excellence in England (E3) fund and the Medical Research Council. T.J.M. (McKinley) is also supported by the Wellcome Trust. S.M. is funded by a Welcome Trust Career Development Award (grant 223024/Z/21/Z) and is supported by the Imperial NIHR BRC. S.J. is funded by a Medical Research Council Clinical Training Research Fellowship. K.A.P. is funded by the Wellcome Trust (219606/Z/19/Z). This study has been delivered through the National Institute for Health and Care Research (NIHR) Exeter Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the Medical Research Council, the NIHR or the Department of Health and Social Care
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