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Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes
Author(s) -
Zhao Lue Ping,
Carlsson Annelie,
Larsson Helena Elding,
Forsander Gun,
Ivarsson Sten A.,
Kockum Ingrid,
Ludvigsson Johnny,
Marcus Claude,
Persson Martina,
Samuelsson Ulf,
Örtqvist Eva,
Pyo ChulWoo,
Bolouri Hamid,
Zhao Michael,
Nelson Wyatt C.,
Geraghty Daniel E.,
Lernmark Åke
Publication year - 2017
Publication title -
diabetes/metabolism research and reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.307
H-Index - 110
eISSN - 1520-7560
pISSN - 1520-7552
DOI - 10.1002/dmrr.2921
Subject(s) - receiver operating characteristic , type 1 diabetes , medicine , framingham risk score , risk assessment , population , human leukocyte antigen , predictive modelling , diabetes mellitus , statistics , computer science , machine learning , mathematics , immunology , disease , endocrinology , computer security , environmental health , antigen
Aim It is of interest to predict possible lifetime risk of type 1 diabetes (T1D) in young children for recruiting high‐risk subjects into longitudinal studies of effective prevention strategies. Methods Utilizing a case‐control study in Sweden, we applied a recently developed next generation targeted sequencing technology to genotype class II genes and applied an object‐oriented regression to build and validate a prediction model for T1D. Results In the training set, estimated risk scores were significantly different between patients and controls ( P = 8.12 × 10 −92 ), and the area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was 0.917. Using the validation data set, we validated the result with AUC of 0.886. Combining both training and validation data resulted in a predictive model with AUC of 0.903. Further, we performed a “biological validation” by correlating risk scores with 6 islet autoantibodies, and found that the risk score was significantly correlated with IA‐2A (Z‐score = 3.628, P < 0.001). When applying this prediction model to the Swedish population, where the lifetime T1D risk ranges from 0.5% to 2%, we anticipate identifying approximately 20 000 high‐risk subjects after testing all newborns, and this calculation would identify approximately 80% of all patients expected to develop T1D in their lifetime. Conclusion Through both empirical and biological validation, we have established a prediction model for estimating lifetime T1D risk, using class II HLA. This prediction model should prove useful for future investigations to identify high‐risk subjects for prevention research in high‐risk populations.