Development of a clinical calculator to aid the identification of MODY in pediatric patients at the time of diabetes diagnosis

Maturity Onset Diabetes of the Young (MODY) is a young-onset, monogenic form of diabetes without needing insulin treatment. Diagnostic testing is expensive. To aid decisions on who to test, we aimed to develop a MODY probability calculator for paediatric cases at the time of diabetes diagnosis, when...

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Veröffentlicht in:Scientific reports 2024-05, Vol.14 (1), p.10589-10589, Article 10589
Hauptverfasser: Shields, Beverley M., Carlsson, Annelie, Patel, Kashyap, Knupp, Julieanne, Kaur, Akaal, Johnston, Des, Colclough, Kevin, Larsson, Helena Elding, Forsander, Gun, Samuelsson, Ulf, Hattersley, Andrew, Ludvigsson, Johnny
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Sprache:eng
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Zusammenfassung:Maturity Onset Diabetes of the Young (MODY) is a young-onset, monogenic form of diabetes without needing insulin treatment. Diagnostic testing is expensive. To aid decisions on who to test, we aimed to develop a MODY probability calculator for paediatric cases at the time of diabetes diagnosis, when the existing “MODY calculator” cannot be used. Firth logistic regression models were developed on data from 3541 paediatric patients from the Swedish ‘Better Diabetes Diagnosis’ (BDD) population study (n = 46 (1.3%) MODY ( HNF1A, HNF4A, GCK )). Model performance was compared to using islet autoantibody testing. HbA1c, parent with diabetes, and absence of polyuria were significant independent predictors of MODY. The model showed excellent discrimination (c-statistic = 0.963) and calibrated well (Brier score = 0.01). MODY probability > 1.3% (ie. above background prevalence) had similar performance to being negative for all 3 antibodies (positive predictive value (PPV) = 10% v 11% respectively i.e. ~ 1 in 10 positive test rate). Probability > 1.3% and negative for 3 islet autoantibodies narrowed down to 4% of the cohort, and detected 96% of MODY cases (PPV = 31%). This MODY calculator for paediatric patients at time of diabetes diagnosis will help target genetic testing to those most likely to benefit, to get the right diagnosis.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-60160-0