University of Oulu

Ng, K., Anand, V., Stavropoulos, H. et al. Quantifying the utility of islet autoantibody levels in the prediction of type 1 diabetes in children. Diabetologia 66, 93–104 (2023).

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:
Language: English
Published: Springer Nature, 2023
Publish Date: 2023-09-08


Aims/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.

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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
Type of Publication: A1 Journal article – refereed
Field of Science: 3121 General medicine, internal medicine and other clinical medicine
3123 Gynaecology and paediatrics
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.
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