Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes
Correction(s) for this article
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Corrigendum
- Volume 34Issue 2Diabetes/Metabolism Research and Reviews
- First Published online: February 15, 2018
Corresponding Author
Lue Ping Zhao
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
School of Public Health, University of Washington, Seattle, WA, USA
Correspondence
Lue Ping Zhao, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave NE, Seattle, WA 98109, USA.
Email: [email protected]
Search for more papers by this authorAnnelie Carlsson
Department of Pediatrics, Lund University, Lund, Sweden
Search for more papers by this authorHelena Elding Larsson
Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital, Malmö, Sweden
Search for more papers by this authorGun Forsander
Institute of Clinical Sciences, Department of Pediatrics and the Queen Silvia Children's Hospital, Sahlgrenska University Hospital, Gothenburg, Sweden
Search for more papers by this authorSten A. Ivarsson
Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital, Malmö, Sweden
Search for more papers by this authorIngrid Kockum
Department of Clinical Neurosciences, Karolinska Institutet, Solna, Sweden
Search for more papers by this authorJohnny Ludvigsson
Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
Search for more papers by this authorClaude Marcus
Department of Clinical Science, Karolinska Institutet, Huddinge, Sweden
Search for more papers by this authorMartina Persson
Department of Medicine, Clinical Epidemiology, Karolinska University Hospital, Solna, Sweden
Search for more papers by this authorUlf Samuelsson
Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
Search for more papers by this authorEva Örtqvist
Department of Medicine, Clinical Epidemiology, Karolinska University Hospital, Solna, Sweden
Search for more papers by this authorChul-Woo Pyo
Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
Search for more papers by this authorHamid Bolouri
School of Arts and Sciences, University of Washington, Seattle, WA, USA
Search for more papers by this authorMichael Zhao
School of Arts and Sciences, University of Washington, Seattle, WA, USA
Search for more papers by this authorWyatt C. Nelson
Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
Search for more papers by this authorDaniel E. Geraghty
Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
Search for more papers by this authorÅke Lernmark
Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital, Malmö, Sweden
Search for more papers by this authorThe Better Diabetes Diagnosis (BDD) Study Group
Search for more papers by this authorCorresponding Author
Lue Ping Zhao
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
School of Public Health, University of Washington, Seattle, WA, USA
Correspondence
Lue Ping Zhao, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave NE, Seattle, WA 98109, USA.
Email: [email protected]
Search for more papers by this authorAnnelie Carlsson
Department of Pediatrics, Lund University, Lund, Sweden
Search for more papers by this authorHelena Elding Larsson
Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital, Malmö, Sweden
Search for more papers by this authorGun Forsander
Institute of Clinical Sciences, Department of Pediatrics and the Queen Silvia Children's Hospital, Sahlgrenska University Hospital, Gothenburg, Sweden
Search for more papers by this authorSten A. Ivarsson
Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital, Malmö, Sweden
Search for more papers by this authorIngrid Kockum
Department of Clinical Neurosciences, Karolinska Institutet, Solna, Sweden
Search for more papers by this authorJohnny Ludvigsson
Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
Search for more papers by this authorClaude Marcus
Department of Clinical Science, Karolinska Institutet, Huddinge, Sweden
Search for more papers by this authorMartina Persson
Department of Medicine, Clinical Epidemiology, Karolinska University Hospital, Solna, Sweden
Search for more papers by this authorUlf Samuelsson
Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
Search for more papers by this authorEva Örtqvist
Department of Medicine, Clinical Epidemiology, Karolinska University Hospital, Solna, Sweden
Search for more papers by this authorChul-Woo Pyo
Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
Search for more papers by this authorHamid Bolouri
School of Arts and Sciences, University of Washington, Seattle, WA, USA
Search for more papers by this authorMichael Zhao
School of Arts and Sciences, University of Washington, Seattle, WA, USA
Search for more papers by this authorWyatt C. Nelson
Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
Search for more papers by this authorDaniel E. Geraghty
Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
Search for more papers by this authorÅke Lernmark
Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital, Malmö, Sweden
Search for more papers by this authorThe Better Diabetes Diagnosis (BDD) Study Group
Search for more papers by this authorAbstract
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.
Supporting Information
| Filename | Description |
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| DMRR_2921-Sup-0001-Figure 5S.tiffTIFF image, 16.5 MB |
Data S1 Supporting info item |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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