University of Oulu

Ghalwash, M., Dunne, J. L., Lundgren, M., Rewers, M., Ziegler, A.-G., Anand, V., Toppari, J., Veijola, R., & Hagopian, W. (2022). Two-age islet-autoantibody screening for childhood type 1 diabetes: A prospective cohort study. The Lancet Diabetes & Endocrinology, 10(8), 589–596.

Two-age islet-autoantibody screening for childhood type 1 diabetes : a prospective cohort study

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Author: Ghalwash, Mohamed1,2; Dunne, Jessica L3; Lundgren, Markus4;
Organizations: 1Center for Computational Health, IBM Research, Yorktown Heights, NY, USA
2Faculty of Science, Ain Shams University, Cairo, Egypt
3Juvenile Diabetes Research Foundation, New York, NY, USA
4Department of Clinical Sciences Malmö, Lund University/Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
5Barbara Davis Center for Diabetes, University of Colorado, Denver, CO, USA
6Forschegruppe Diabetes and Institute of Diabetes Research, Helmholtz Zentrum München, German Research Centre for Environmental Health, Munich-Neuherberg, Germany der TU München, Munich, Germany
7Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, and Centre for Population Health Research, University of Turku, Turku, Finland
8Department of Paediatrics, Turku University Hospital, Turku, Finland
9Department of Paediatrics, PEDEGO Research Unit, University of Oulu and Oulu University Hospital, Oulu, Finland
10Pacific Northwest Research Institute, Seattle, WA, USA
11Department of Medicine, University of Washington, Seattle, WA, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
Persistent link:
Language: English
Published: Elsevier, 2022
Publish Date: 2023-03-14


Background: Early prediction of childhood type 1 diabetes reduces ketoacidosis at diagnosis and provides opportunities for disease prevention. However, only highly efficient approaches are likely to succeed in public health settings. We sought to identify efficient strategies for initial islet autoantibody screening in children younger than 15 years.

Methods: We harmonised data from five prospective cohorts from Finland (DIPP), Germany (BABYDIAB), Sweden (DiPiS), and the USA (DAISY and DEW-IT) into the Type 1 Diabetes Intelligence (T1DI) cohort. 24 662 children at high risk of diabetes enrolled before age 2 years were included and followed up for islet autoantibodies and diabetes until age 15 years, or type 1 diabetes onset, whichever occurred first. Islet autoantibodies measured included those against glutamic acid decarboxylase, insulinoma antigen 2, and insulin. Main outcomes were sensitivity and positive predictive value (PPV) of detected islet autoantibodies, tested at one or two fixed ages, for diagnosis of clinical type 1 diabetes.

Findings: Of the 24 662 participants enrolled in the Type 1 Diabetes Intelligence cohort, 6722 total were followed up to age 15 years or until onset of type 1 diabetes. Type 1 diabetes developed by age 15 years in 672 children, but did not develop in 6050 children. Optimal screening ages for two measurements were 2 years and 6 years, yielding sensitivity of 82% (95% CI 79–86) and PPV of 79% (95% CI 75–80) for diabetes by age 15 years. Autoantibody positivity at the beginning of each test age was highly predictive of diagnosis in the subsequent 2–5·99 year or 6–15-year age intervals. Autoantibodies usually appeared before age 6 years even in children diagnosed with diabetes much later in childhood.

Interpretation: Our results show that initial screening for islet autoantibodies at two ages (2 years and 6 years) is sensitive and efficient for public health translation but might require adjustment by country on the basis of population-specific disease characteristics.

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Series: The Lancet. Diabetes & endocrinology
ISSN: 2213-8587
ISSN-E: 2213-8595
ISSN-L: 2213-8587
Volume: 10
Issue: 8
Pages: 589 - 596
DOI: 10.1016/s2213-8587(22)00141-3
Type of Publication: A1 Journal article – refereed
Field of Science: 3121 General medicine, internal medicine and other clinical medicine
3123 Gynaecology and paediatrics
Funding: This work was supported by funding from JDRF (IBM: 1-RSC-2017-368-I-X, 1-IND-2019-717-I-X), (DAISY: 1-SRA-2019-722-I-X, 1-RSC-2017-517-I-X, 5-ECR-2017-388-A-N), (DiPiS: 1-SRA-2019-720-I-X, 1-RSC-2017-526-I-X), (DIPP: 1-RSC-2018-555-I-X, 1-SRA-2019-721-I-X), (DEW-IT: 1-RSC-2017-516-I-X, 1-SRA-2019-719-I-X).
Copyright information: © 2022 This manuscript version is made available under the CC-BY-NC-ND 4.0 license