Quantifying the utility of islet autoantibody levels in the prediction of type 1 diabetes in children
(2023) In Diabetologia 66(1). p.93-104- Abstract
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.... (More)
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. Graphical abstract: [Figure not available: see fulltext.]
(Less)
- author
- author collaboration
- organization
- publishing date
- 2023-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Islet autoantibody levels, Machine learning, Risk prediction models, Type 1 diabetes
- in
- Diabetologia
- volume
- 66
- issue
- 1
- pages
- 12 pages
- publisher
- Springer
- external identifiers
-
- pmid:36195673
- scopus:85139222497
- ISSN
- 0012-186X
- DOI
- 10.1007/s00125-022-05799-y
- language
- English
- LU publication?
- yes
- id
- 47964497-d34a-49c3-bc1e-c756153379ce
- date added to LUP
- 2022-12-14 12:56:38
- date last changed
- 2024-04-14 10:56:58
@article{47964497-d34a-49c3-bc1e-c756153379ce,
abstract = {{<p>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. Graphical abstract: [Figure not available: see fulltext.]</p>}},
author = {{Ng, Kenney and Anand, Vibha and Stavropoulos, Harry and Veijola, Riitta and Toppari, Jorma and Maziarz, Marlena and Lundgren, Markus and Waugh, Kathy and Frohnert, Brigitte I. and Martin, Frank and Lou, Olivia and Hagopian, William and Achenbach, Peter}},
issn = {{0012-186X}},
keywords = {{Islet autoantibody levels; Machine learning; Risk prediction models; Type 1 diabetes}},
language = {{eng}},
number = {{1}},
pages = {{93--104}},
publisher = {{Springer}},
series = {{Diabetologia}},
title = {{Quantifying the utility of islet autoantibody levels in the prediction of type 1 diabetes in children}},
url = {{http://dx.doi.org/10.1007/s00125-022-05799-y}},
doi = {{10.1007/s00125-022-05799-y}},
volume = {{66}},
year = {{2023}},
}