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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

Zhao, Lue Ping; Carlsson, Annelie LU ; Larsson, Helena Elding LU ; Forsander, Gun; Ivarsson, Sten A LU ; Kockum, Ingrid; Ludvigsson, Johnny; Marcus, Claude; Persson, Martina and Samuelsson, Ulf, et al. (2017) In Diabetes/Metabolism Research and Reviews 33(8).
Abstract

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... (More)

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.

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Journal Article
in
Diabetes/Metabolism Research and Reviews
volume
33
issue
8
publisher
John Wiley & Sons
external identifiers
  • scopus:85029441374
  • wos:000414371300004
ISSN
1520-7552
DOI
10.1002/dmrr.2921
language
English
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yes
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7b74e422-bbfa-4bde-9dfc-65e691a0a08c
date added to LUP
2017-09-21 11:24:01
date last changed
2018-01-16 13:26:08
@article{7b74e422-bbfa-4bde-9dfc-65e691a0a08c,
  abstract     = {<p>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.</p><p>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.</p><p>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 &lt; 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.</p><p>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.</p>},
  articleno    = {e2921},
  author       = {Zhao, Lue Ping and Carlsson, Annelie and Larsson, Helena Elding and Forsander, Gun and Ivarsson, Sten A and Kockum, Ingrid and Ludvigsson, Johnny and Marcus, Claude and Persson, Martina and Samuelsson, Ulf and Örtqvist, Eva and Pyo, Chul-Woo and Bolouri, Hamid and Zhao, Michael and Nelson, Wyatt C and Geraghty, Daniel E and Lernmark, Åke and , },
  issn         = {1520-7552},
  keyword      = {Journal Article},
  language     = {eng},
  month        = {07},
  number       = {8},
  publisher    = {John Wiley & Sons},
  series       = {Diabetes/Metabolism Research and Reviews},
  title        = {Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes},
  url          = {http://dx.doi.org/10.1002/dmrr.2921},
  volume       = {33},
  year         = {2017},
}