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We explored the association between growth features and T1D development using landmark analysis at different ages. Analysis included individuals from 2 birth cohort studies: DAISY and BABYDIAB (n=2,664; 129 progressed to T1D). Using height and weight measured over time, percentiles for age were calculated. Missing values were imputed using LMS parameters of CDC growth charts. Rates of change of percentiles were computed over the prior year. Twelve ages (1-12 years) were used for landmark analysis with Random Survival Forest to predict probability of T1D onset in the 1- to 19-year follow up windows. The baseline model used HLA risk group, sex, T1D family history and breastfeeding history. The full model added growth features: height and weight percentiles at age and change in percentiles. Performance was measured using C-index and feature importance was ranked. Incorporating growth features significantly improved prediction accuracy of T1D onset for 95% of combinations of landmark ages and prediction window sizes (Table 1, not all ages and windows shown). The order of features from most to least predictive is: HLA group, rates of height and weight changes, height and weight percentiles, family history, breastfeeding and sex. This analysis demonstrates that using growth features can significantly improve prediction of T1D. Table 1: C-Index with and without inclusion of growth features for landmark age of 3 years. Disclosure: Z. Li: None. V. Anand: None. J.L. Dunne: None. B.I. Frohnert: None. W. Hagopian: Consultant; Self; Novo Nordisk Inc. H. Hyoty: None. M. Maziarz: None. A. Ziegler: None. J. Toppari: None. Funding: JDRF (1-IND-2019-717-I-X, 1-SRA-2019-722-I-X, 1-SRA-2019-720-I-X, 1-SRA-2019-721-I-X, 1-SRA-2019-719-I-X, 1-SRA-2019-723-I-X) [ABSTRACT FROM AUTHOR]

We investigated evolution of islet autoantibodies (IAs) prior to onset of T1D from 5 large-scale birth cohort studies. Our analysis revealed three distinct IA trajectories leading up to diagnosis of T1D. Of 24673 children from five prospective studies (DAISY, DiPiS, DIPP, DEW-IT, and BABYDIAB), 688 who were diagnosed with T1D and had 3 or more visits were included in this analysis. Hidden Markov Models were developed to label visit-level observation of each subject based on three IAs: GADA, IAA, and IA-2A. Interactive visualizations were then applied to explore model outcomes, identify IA evolution trajectories, and examine their clinical characteristics. Three trajectories were identified (Figure 1) with a majority of children having multiple IA (Tr1: n=265) or IAA first (Tr2: n=282) at seroconversion; the minority seroconverted with GADA first (Tr3: n=131). The Tr3 group had seroconversion and T1D onset at an older age in months (58, 132) than Tr1 (42, 96) and Tr2 (31, 88), P <.01. Distribution of HLA DR/DQ differed between groups: higher DRX/X and lower DR3/4 in Tr3 (18%, 24%) than Tr1 (11%, 26%) and Tr2 (10%, 31%). The three IA trajectories show distinctive antibody patterns, ages of seroconversion and T1D onset and HLA DR/DQ group distributions among them. Furthermore, heterogeneity is also shown within each trajectory in terms of progression time and needs further investigation. Disclosure: B. Kwon: None. P. Achenbach: None. V. Anand: None. J.L. Dunne: None. W. Hagopian: Consultant; Self; Novo Nordisk Inc. M. Lundgren: None. R. Veijola: None. B.I. Frohnert: None. Funding: JDRF (1-IND-2019-717-I-X, 1-SRA-2019-722-I-X, 1-SRA-2019-723-I-X, 1-SRA-2019-719-I-X, 1-SRA-2019-721-I-X, 1-SRA-2019-720-I-X) [ABSTRACT FROM AUTHOR]

The development of multiple islet autoantibodies (IA) predicts progression to type 1 diabetes (T1D), and forms the basis of current staging of T1D. However, age at seroconversion, persistence of IA, and frequency of sampling influence the determination of multiple IA status. Individual IA may appear at different timepoints and fluctuate. This analysis aims to understand how differences in the stringency of multiple IA status definition impact risk estimates of progression to stage 3 T1D. We combined data from three birth-cohort studies (N=18,537): DAISY (U.S.), DiPiS (Sweden) and DIPP (Finland), defining four groups by IA positivity pattern and accounting for variability in persistence and timing in concurrent and sequential patterns of IAA, IA2A, and GADA. Subjects were placed exclusively in their most stringent IA group. Cumulative incidence of progression to T1D was estimated by survival analysis and log-rank test, which showed group differences (p<0.0001). T1D risk 15 years after seroconversion [95% CI] increased according to increasing stringency of the definition: Single IA/Persistent 10% [6%, 14%], multiple IA/Any 17% [3%, 29%], multiple IA/Same Visit 40% [17%, 57%], and multiple IA/Persistent 74% [69%, 77%]. Enhanced understanding of the characteristics of IA patterns can better inform recruitment for intervention studies and improve communication of risk during pre-symptomatic T1D screening. Disclosure: B. Liu: None. M. Ghalwash: None. V. Anand: None. E. Koski: None. K. Ng: None. J.L. Dunne: None. M. Lundgren: None. H. Elding Larsson: None. W. Hagopian: Research Support; Self; Novo Nordisk A/S. R. Veijola: None. B.I. Frohnert: None. Funding: JDRF [ABSTRACT FROM AUTHOR]

Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programmes are being increasingly emphasised. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb + ) children and adults who are at risk of (confirmed single IAb + ) or living with (multiple IAb + ) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in non-specialised settings. To inform this monitoring, JDRF in conjunction with international experts and societies developed consensus guidance. Broad advice from this guidance includes the following: (1) partnerships should be fostered between endocrinologists and primary-care providers to care for people who are IAb + ; (2) when people who are IAb + are initially identified there is a need for confirmation using a second sample; (3) single IAb + individuals are at lower risk of progression than multiple IAb + individuals; (4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; (5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and (6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasises significant unmet needs for further research on early-stage type 1 diabetes to increase the rigour of future recommendations and inform clinical care., (© 2024. American Diabetes Association and European Association for the Study of Diabetes.)

Objective: To assess anxiety and risk perception among parents whose children screened positive for islet autoantibodies, indicating elevated risk for type 1 diabetes (T1D)., Research Design and Methods: The Autoimmunity Screening for Kids (ASK) study identified 319 children age 1 to 17 years at risk for T1D via screening for islet autoantibodies; 280 children with confirmed islet autoantibodies and their caregivers enrolled in a follow-up education and monitoring program to prevent diabetic ketoacidosis at diagnosis. Parents completed questionnaires at each monitoring visit, including a 6-item version of the State Anxiety Inventory (SAI), to assess anxiety about their child developing T1D, and a single question to assess risk perception., Results: At the first ASK follow-up monitoring visit, mean parental anxiety was elevated above the clinical cutoff of 40 (SAI 46.1 ± 11.2). At the second follow-up monitoring visit (i.e., visit 2), mean anxiety remained elevated but started to trend down. Approximately half (48.9%) of parents reported their child was at increased risk for T1D at the initial follow-up monitoring visit (visit 1). Parents of children with more than one islet autoantibody and a first-degree relative with T1D were more likely to report their child was at increased risk., Conclusions: Most parents of autoantibody-positive children have high anxiety about their child developing T1D. Information about the risk of developing T1D is difficult to convey, as evidenced by the wide range of risk perception reported in this sample., (© 2023 by the American Diabetes Association.)

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., Competing Interests: Declaration of interests The authors declare no competing interests., (Published by Elsevier Inc.)

The development of medical products that can delay or prevent progression to stage 3 type 1 diabetes faces many challenges. Of note, optimising patient selection for type 1 diabetes prevention clinical trials is hindered by significant patient heterogeneity and a lack of characterisation of the time-varying probability of progression to stage 3 type 1 diabetes in individuals positive for two or more islet autoantibodies. To meet these needs, the Critical Path Institute's Type 1 Diabetes Consortium was launched in 2017 as a pre-competitive public-private partnership between stakeholders from the pharmaceutical industry, patient advocacy groups, philanthropic organisations, clinical researchers, the National Institutes of Health and the Food and Drug Administration. The Type 1 Diabetes Consortium acquired and aggregated data from three longitudinal observational studies, Environmental Determinants of Diabetes in the Young (TEDDY), Diabetes Autoimmunity Study in the Young (DAISY) and TrialNet Pathway to Prevention (TN01), and used analysis subsets of these data to support the model-based qualification of islet autoantibodies as enrichment biomarkers for patient selection in type 1 diabetes prevention trials, including registration studies. The Type 1 Diabetes Consortium has now received a qualification opinion from the European Medicines Agency for the use of these biomarkers, a major success for the field of type 1 diabetes. This endorsement will improve product developers' ability to design clinical trials of agents intended to prevent or delay type 1 diabetes that are reduced in size and/or length, while being adequately powered., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Oxylipins, pro-inflammatory and pro-resolving lipid mediators, are associated with the risk of type 1 diabetes (T1D) and may be influenced by diet. This study aimed to develop a nutrient pattern related to oxylipin profiles and test their associations with the risk of T1D among youth. The nutrient patterns were developed with a reduced rank regression in a nested case-control study ( n = 335) within the Diabetes Autoimmunity Study in the Young (DAISY), a longitudinal cohort of children at risk of T1D. The oxylipin profiles (adjusted for genetic predictors) were the response variables. The nutrient patterns were tested in the case-control study ( n = 69 T1D cases, 69 controls), then validated in the DAISY cohort using a joint Cox proportional hazards model ( n = 1933, including 81 T1D cases). The first nutrient pattern (NP1) was characterized by low beta cryptoxanthin, flavanone, vitamin C, total sugars and iron, and high lycopene, anthocyanidins, linoleic acid and sodium. After adjusting for T1D family history, the HLA genotype, sex and race/ethnicity, NP1 was associated with a lower risk of T1D in the nested case-control study (OR: 0.44, p = 0.0126). NP1 was not associated with the risk of T1D (HR: 0.54, p -value = 0.1829) in the full DAISY cohort. Future studies are needed to confirm the nested case-control findings and investigate the modifiable factors for oxylipins.

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., (© 2022. The Author(s).)

This study investigates a missing value imputation approach for longitudinal growth data in pediatric studies from multiple countries. We analyzed a combined cohort from five natural history studies of type 1 diabetes (T1D) in the US and EU with longitudinal growth measurements for 23,201 subjects. We developed a multiple imputation methodology using LMS parameters of CDC reference data. We measured imputation errors on both combined and individual cohorts using mean absolute percentage error (MAPE) and normalized root-mean-square error (NRMSE). Our results show low imputation errors using CDC reference. Overall height imputation errors were lower than for weight. The largest MAPE for weight and height among all age groups was 4.8% and 1.7%, respectively. When comparing performance between CDC reference and country-specific growth charts, we found no significant differences for height (CDC vs. German: p =0.993, CDC vs. Swedish: p=0.368) and for weight (CDC vs. Swedish: p=0.513) for all ages., (©2021 AMIA - All rights reserved.)

Aims/hypothesis: Oxylipins are lipid mediators derived from polyunsaturated fatty acids. Some oxylipins are proinflammatory (e.g. those derived from arachidonic acid [ARA]), others are pro-resolving of inflammation (e.g. those derived from α-linolenic acid [ALA], docosahexaenoic acid [DHA] and eicosapentaenoic acid [EPA]) and others may be both (e.g. those derived from linoleic acid [LA]). The goal of this study was to examine whether oxylipins are associated with incident type 1 diabetes., Methods: We conducted a nested case-control analysis in the Diabetes Autoimmunity Study in the Young (DAISY), a prospective cohort study of children at risk of type 1 diabetes. Plasma levels of 14 ARA-derived oxylipins, ten LA-derived oxylipins, six ALA-derived oxylipins, four DHA-derived oxylipins and two EPA-related oxylipins were measured by ultra-HPLC-MS/MS at multiple timepoints related to autoantibody seroconversion in 72 type 1 diabetes cases and 71 control participants, which were frequency matched on age at autoantibody seroconversion (of the case), ethnicity and sample availability. Linear mixed models were used to obtain an age-adjusted mean of each oxylipin prior to type 1 diabetes. Age-adjusted mean oxylipins were tested for association with type 1 diabetes using logistic regression, adjusting for the high risk HLA genotype HLA-DR3/4,DQB1*0302. We also performed principal component analysis of the oxylipins and tested principal components (PCs) for association with type 1 diabetes. Finally, to investigate potential critical timepoints, we examined the association of oxylipins measured before and after autoantibody seroconversion (of the cases) using PCs of the oxylipins at those visits., Results: The ARA-related oxylipin 5-HETE was associated with increased type 1 diabetes risk. Five LA-related oxylipins, two ALA-related oxylipins and one DHA-related oxylipin were associated with decreased type 1 diabetes risk. A profile of elevated LA- and ALA-related oxylipins (PC1) was associated with decreased type 1 diabetes risk (OR 0.61; 95% CI 0.40, 0.94). A profile of elevated ARA-related oxylipins (PC2) was associated with increased diabetes risk (OR 1.53; 95% CI 1.03, 2.29). A critical timepoint analysis showed type 1 diabetes was associated with a high ARA-related oxylipin profile at post-autoantibody-seroconversion but not pre-seroconversion., Conclusions/interpretation: The protective association of higher LA- and ALA-related oxylipins demonstrates the importance of both inflammation promotion and resolution in type 1 diabetes. Proinflammatory ARA-related oxylipins may play an important role once the autoimmune process has begun.

Analyzing disease progression patterns can provide useful insights into the disease processes of many chronic conditions. These analyses may help inform recruitment for prevention trials or the development and personalization of treatments for those affected. We learn disease progression patterns using Hidden Markov Models (HMM) and distill them into distinct trajectories using visualization methods. We apply it to the domain of Type 1 Diabetes (T1D) using large longitudinal observational data from the T1DI study group. Our method discovers distinct disease progression trajectories that corroborate with recently published findings. In this paper, we describe the iterative process of developing the model. These methods may also be applied to other chronic conditions that evolve over time., (©2020 AMIA - All rights reserved.)