| 1. |
- Frohnert, Brigitte I., et al.
(author)
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Refining the Definition of Stage 1 Type 1 Diabetes : An Ontology-Driven Analysis of the Heterogeneity of Multiple Islet Autoimmunity
- 2023
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In: Diabetes Care. - : American Diabetes Association. - 0149-5992 .- 1935-5548. ; 46:10, s. 1753-1761
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Journal article (peer-reviewed)abstract
- OBJECTIVE To estimate the risk of progression to stage 3 type 1 diabetes based on varying definitions of multiple islet autoantibody positivity (mIA). RESEARCH DESIGN AND METHODS Type 1 Diabetes Intelligence (T1DI) is a combined prospective data set of children from Finland, Germany, Sweden, and the U.S. who have an increased genetic risk for type 1 diabetes. Analysis included 16,709 infants-toddlers enrolled by age 2.5 years and comparison between groups using Kaplan-Meier survival analysis. RESULTS Of 865 (5%) children with mIA, 537 (62%) progressed to type 1 diabetes. The 15-year cumulative incidence of diabetes varied from the most stringent definition (mIA/ Persistent/2: two or more islet autoantibodies positive at the same visit with two or more antibodies persistent at next visit; 88% [95% CI 85–92%]) to the least stringent (mIA/Any: positivity for two islet autoantibodies without co-occurring positivity or persistence; 18% [5–40%]). Progression in mIA/Persistent/2 was significantly higher than all other groups (P < 0.0001). Intermediate stringency definitions showed intermediate risk and were significantly different than mIA/Any (P < 0.05); however, differences waned over the 2-year follow-up among those who did not subsequently reach higher stringency. Among mIA/Persistent/2 individuals with three autoantibodies, loss of one autoantibody by the 2-year follow-up was associated with accelerated progression. Age was significantly associated with time from seroconversion to mIA/ Persistent/2 status and mIA to stage 3 type 1 diabetes. CONCLUSIONS The 15-year risk of progression to type 1 diabetes risk varies markedly from 18 to 88% based on the stringency of mIA definition. While initial categorization identifies highest-risk individuals, short-term follow-up over 2 years may help stratify evolving risk, especially for those with less stringent definitions of mIA.
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| 2. |
- Ghalwash, Mohamed, et al.
(author)
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Islet autoantibody screening in at-risk adolescents to predict type 1 diabetes until young adulthood : a prospective cohort study
- 2023
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In: The Lancet Child and Adolescent Health. - 2352-4642. ; 7:4, s. 261-268
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Journal article (peer-reviewed)abstract
- Background: Screening for islet autoantibodies in children and adolescents identifies individuals who will later develop type 1 diabetes, allowing patient and family education to prevent diabetic ketoacidosis at onset and to enable consideration of preventive therapies. We aimed to assess whether islet autoantibody screening is effective for predicting type 1 diabetes in adolescents aged 10−18 years with an increased risk of developing type 1 diabetes. Methods: Data were harmonised from prospective studies from Finland (the Diabetes Prediction and Prevention study), Germany (the BABYDIAB study), and the USA (Diabetes Autoimmunity Study in the Young and the Diabetes Evaluation in Washington study). Autoantibodies against insulin, glutamic acid decarboxylase, and insulinoma-associated protein 2 were measured at each follow-up visit. Children who were lost to follow-up or diagnosed with type 1 diabetes before 10 years of age were excluded. Inverse probability censoring weighting was used to include data from remaining participants. Sensitivity and the positive predictive value of these autoantibodies, tested at one or two ages, to predict type 1 diabetes by the age of 18 years were the main outcomes. Findings: Of 20 303 children with an increased type 1 diabetes risk, 8682 were included for the analysis with inverse probability censoring weighting. 1890 were followed up to 18 years of age or developed type 1 diabetes between the ages of 10 years and 18 years, and their median follow-up was 18·3 years (IQR 14·5–20·3). 442 (23·4%) of 1890 adolescents were positive for at least one islet autoantibody, and 262 (13·9%) developed type 1 diabetes. Time from seroconversion to diabetes diagnosis increased by 0·64 years (95% CI 0·34–0·95) for each 1-year increment of diagnosis age (Pearson's correlation coefficient 0·88, 95% CI 0·50–0·97, p=0·0020). The median interval between the last prediagnostic sample and diagnosis was 0·3 years (IQR 0·1–1·3) in the 227 participants who were autoantibody positive and 6·8 years (1·6–9·9) for the 35 who were autoantibody negative. Single screening at the age of 10 years was 90% (95% CI 86–95) sensitive, with a positive predictive value of 66% (60–72) for clinical diabetes. Screening at two ages (10 years and 14 years) increased sensitivity to 93% (95% CI 89–97) but lowered the positive predictive value to 55% (49–60). Interpretation: Screening of adolescents at risk for type 1 diabetes only once at 10 years of age for islet autoantibodies was highly effective to detect type 1 diabetes by the age of 18 years, which in turn could enable prevention of diabetic ketoacidosis and participation in secondary prevention trials. Funding: JDRF International.
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| 3. |
- Nakayasu, Ernesto S, et al.
(author)
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Plasma protein biomarkers predict the development of persistent autoantibodies and type 1 diabetes 6 months prior to the onset of autoimmunity
- 2023
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In: Cell Reports Medicine. - 2666-3791. ; 4:7
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Journal article (peer-reviewed)abstract
- Type 1 diabetes (T1D) results from autoimmune destruction of β cells. Insufficient availability of biomarkers represents a significant gap in understanding the disease cause and progression. We conduct blinded, two-phase case-control plasma proteomics on the TEDDY study to identify biomarkers predictive of T1D development. Untargeted proteomics of 2,252 samples from 184 individuals identify 376 regulated proteins, showing alteration of complement, inflammatory signaling, and metabolic proteins even prior to autoimmunity onset. Extracellular matrix and antigen presentation proteins are differentially regulated in individuals who progress to T1D vs. those that remain in autoimmunity. Targeted proteomics measurements of 167 proteins in 6,426 samples from 990 individuals validate 83 biomarkers. A machine learning analysis predicts if individuals would remain in autoimmunity or develop T1D 6 months before autoantibody appearance, with areas under receiver operating characteristic curves of 0.871 and 0.918, respectively. Our study identifies and validates biomarkers, highlighting pathways affected during T1D development.
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| 4. |
- Ng, Kenney, et al.
(author)
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Quantifying the utility of islet autoantibody levels in the prediction of type 1 diabetes in children
- 2023
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In: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; 66:1, s. 93-104
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Journal article (peer-reviewed)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. 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.]
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