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Quantifying the utility of islet autoantibody levels in the prediction of type 1 diabetes in children |
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| Author: | Ng, Kenney1; Anand, Vibha1; Stavropoulos, Harry2; |
| Organizations: |
1BM Research, Cambridge, MA, USA 2IBM Research, Yorktown Heights, NY, USA 3Department of Pediatrics, PEDEGO Research Unit, University of Oulu and Oulu University Hospital, Oulu, Finland
4Institute of Biomedicine and Centre for Population Health Research, University of Turku, Turku, Finland
5Department of Pediatrics, Turku University Hospital, Turku, Finland 6Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden 7Department of Pediatrics, Kristianstad Hospital, Kristianstad, Sweden 8Barbara Davis Center for Diabetes, University of Colorado, Denver, CO, USA 9JDRF International, New York, NY, USA 10Pacific Northwest Research Institute, Seattle, WA, USA 11Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich-Neuherberg, Germany |
| Format: | article |
| Version: | published version |
| Access: | open |
| Online Access: | PDF Full Text (PDF, 1 MB) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe20230908122054 |
| Language: | English |
| Published: |
Springer Nature,
2023
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| Publish Date: | 2023-09-08 |
| Description: |
AbstractAims/hypothesis: The aim of this study was to explore the utility of islet autoantibody (IAb) levels for the prediction of type 1 diabetes in autoantibody-positive children. Methods: Prospective cohort studies in Finland, Germany, Sweden and the USA followed 24,662 children at increased genetic or familial risk of developing islet autoimmunity and diabetes. For the 1403 who developed IAbs (523 of whom developed diabetes), levels of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA) and insulinoma-associated antigen-2 (IA-2A) were harmonised for analysis. Diabetes prediction models using multivariate logistic regression with inverse probability censored weighting (IPCW) were trained using 10-fold cross-validation. Discriminative power for disease was estimated using the IPCW concordance index (C index) with 95% CI estimated via bootstrap. Results: A baseline model with covariates for data source, sex, diabetes family history, HLA risk group and age at seroconversion with a 10-year follow-up period yielded a C index of 0.61 (95% CI 0.58, 0.63). The performance improved after adding the IAb positivity status for IAA, GADA and IA-2A at seroconversion: C index 0.72 (95% CI 0.71, 0.74). Using the IAb levels instead of positivity indicators resulted in even better performance: C index 0.76 (95% CI 0.74, 0.77). The predictive power was maintained when using the IAb levels alone: C index 0.76 (95% CI 0.75, 0.76). The prediction was better for shorter follow-up periods, with a C index of 0.82 (95% CI 0.81, 0.83) at 2 years, and remained reasonable for longer follow-up periods, with a C index of 0.76 (95% CI 0.75, 0.76) at 11 years. Inclusion of the results of a third IAb test added to the predictive power, and a suitable interval between seroconversion and the third test was approximately 1.5 years, with a C index of 0.78 (95% CI 0.77, 0.78) at 10 years follow-up. Conclusions/interpretation: Consideration of quantitative patterns of IAb levels improved the predictive power for type 1 diabetes in IAb-positive children beyond qualitative IAb positivity status. see all
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| Series: |
Diabetologia |
| ISSN: | 0012-186X |
| ISSN-E: | 1432-0428 |
| ISSN-L: | 0012-186X |
| Volume: | 66 |
| Issue: | 1 |
| Pages: | 93 - 104 |
| DOI: | 10.1007/s00125-022-05799-y |
| OADOI: | https://oadoi.org/10.1007/s00125-022-05799-y |
| Type of Publication: |
A1 Journal article – refereed |
| Field of Science: |
3121 General medicine, internal medicine and other clinical medicine 3123 Gynaecology and paediatrics |
| Subjects: | |
| Funding: |
The DAISY study was additionally funded by the US National Institutes of Health (DK032493, DK032083, DK104351, DK116073; DiPiS: DK26190). The DIPP study was additionally funded by the European Union (grant BMH4-CT98-3314), the Novo Nordisk Foundation, the Academy of Finland (decision number 292538 and Centre of Excellence in Molecular Systems Immunology and Physiology Research 2012-2017, decision number 250114), the Special Research Funds for University Hospitals in Finland, the Diabetes Research Foundation, Finland, and the Sigrid Juselius Foundation, Finland. |
| Dataset Reference: |
The data that support the findings of this study are available from each of the five study groups (DiPiS, BABYDIAB, DIPP, DEW-IT and DAISY) but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. However, data are available from the authors upon reasonable request and with permission from the five study groups. |
| Copyright information: |
© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
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https://creativecommons.org/licenses/by/4.0/ |