Updating a Clinical Prediction Model for Identifying Monogenic Diabetes to Include Both Clinical Features and Biomarkers

TY - JOUR

T1 - Updating a Clinical Prediction Model for Identifying Monogenic Diabetes to Include Both Clinical Features and Biomarkers

AU - Knupp, Julieanne

AU - Cardoso, Pedro

AU - Young, Katherine G.

AU - McDonald, Timothy J.

AU - Patel, Kashyap A.

AU - Colclough, Kevin

AU - Pearson, Ewan R.

AU - Jones, Angus G.

AU - Jones, Sophie

AU - Misra, Shivani

AU - Hattersley, Andrew T.

AU - McKinley, Trevelyan J.

AU - Shields, Beverley M.

N1 - Copyright: © 2025 by the American Diabetes Association.

PY - 2025/10/14

Y1 - 2025/10/14

N2 - Objective: 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.Research Design and Methods: 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 = 597) for initial model development (clinical features only) and recalibrated to population data (Using pharmacogeNetics to Improve Treatment in Early-onset Diabetes [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).Results: For early-insulin-treated individuals, the model incorporating biomarkers improved discrimination over using clinical features only (Receiver Operating Characteristic Area Under the Curve [ROCAUC] 0.98 [95% credible interval [CrI] 0.95-0.98] vs. 0.80 [95% CrI 0.71-0.82], P < 0.001) or biomarkers alone (ROCAUC 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 (ROCAUC 0.98 [95% CrI 0.98-0.98] and 0.92 [95% CrI 0.90-0.93] for early- and not-early-insulin-treated individuals, respectively). Using a ≥5% probability threshold to guide testing achieved positive test rates for monogenic diabetes of 16-19%.Conclusions: 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.

AB - Objective: 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.Research Design and Methods: 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 = 597) for initial model development (clinical features only) and recalibrated to population data (Using pharmacogeNetics to Improve Treatment in Early-onset Diabetes [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).Results: For early-insulin-treated individuals, the model incorporating biomarkers improved discrimination over using clinical features only (Receiver Operating Characteristic Area Under the Curve [ROCAUC] 0.98 [95% credible interval [CrI] 0.95-0.98] vs. 0.80 [95% CrI 0.71-0.82], P < 0.001) or biomarkers alone (ROCAUC 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 (ROCAUC 0.98 [95% CrI 0.98-0.98] and 0.92 [95% CrI 0.90-0.93] for early- and not-early-insulin-treated individuals, respectively). Using a ≥5% probability threshold to guide testing achieved positive test rates for monogenic diabetes of 16-19%.Conclusions: 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.

U2 - 10.2337/dc25-1029

DO - 10.2337/dc25-1029

M3 - Article

C2 - 41086125

SN - 0149-5992

JO - Diabetes Care

JF - Diabetes Care

ER -