Search

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.

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

BACKGROUND/OBJECTIVES: Increased level of glycated hemoglobin (HbA1c) is associated with type 1 diabetes onset that in turn is preceded by one to several autoantibodies against the pancreatic islet beta cell autoantigens; insulin (IA), glutamic acid decarboxylase (GAD), islet antigen-2 (IA-2) and zinc transporter 8 (ZnT8). The risk for type 1 diabetes diagnosis increases by autoantibody number. Biomarkers predicting the development of a second or a subsequent autoantibody and type 1 diabetes are needed to predict disease stages and improve secondary prevention trials. This study aimed to investigate whether HbA1c possibly predicts the progression from first to a subsequent autoantibody or type 1 diabetes in healthy children participating in the Environmental Determinants of Diabetes in the Young (TEDDY) study. METHODS: A joint model was designed to assess the association of longitudinal HbA1c levels with the development of first (insulin or GAD autoantibodies) to a second, second to third, third to fourth autoantibody or type 1 diabetes in healthy children prospectively followed from birth until 15 years of age. RESULTS: It was found that increased levels of HbA1c were associated with a higher risk of type 1 diabetes (HR 1.82, 95% CI [1.57-2.10], p

OBJECTIVE: To estimate risk of progression to stage 3 type 1 diabetes based on varying definition of multiple islet autoantibody positivity (mIA). RESEARCH DESIGN AND METHODS: T1DI is a combined prospective dataset of children from Finland, Germany, Sweden and USA who are at 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%) with mIA, 537 (62%) progressed to type 1 diabetes. The 15-year cumulative incidence of diabetes varied from most stringent definition (mIA/Persistent/2: ≥2 islet autoantibodies positive at the same visit with ≥2 antibodies persistent at next visit; 88%, [95% CI: 85-92%]) to 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 CONCLUSIONS: The 15-year risk of progression to type 1 diabetes risk varies markedly from 18-88% based on 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.

Recently, a haplotype of three single-nucleotide polymorphisms (tri-SNP) in intron 1 of the HLA-DRA1 gene was found to be strongly associated with type 1 diabetes risk in HLA-DR3/3 individuals. The tri-SNP reportedly function as "expression quantitative trait loci," modulating HLA-DR and -DQ expression. The aim was to investigate HLA-DRA1 tri-SNPs in relation to extended HLA class II haplotypes and human peripheral blood cell HLA-DQ cell-surface median fluorescence intensity (MFI), the first-appearing islet autoantibody, and autoimmunity burden. A total of 67 healthy subjects (10-15 y) at increased HLA risk for type 1 diabetes and with (

Context Understanding factors involved in the rate of C-peptide decline is needed to tailor therapies for type 1 diabetes (T1D). Objective Evaluate factors associated with rate of C-peptide decline after a T1D diagnosis in young children. Design Observational study. Setting Academic centers. Participants A total of 57 participants from the Environmental Determinants of Diabetes in the Young (TEDDY) study who were enrolled at 3 months of age and followed until T1D, and 56 age-matched children diagnosed with T1D in the community. Intervention A mixed meal tolerance test was used to measure the area under the curve (AUC) C-peptide at 1, 3, 6, 12, and 24 months postdiagnosis. Outcome Factors associated with rate of C-peptide decline during the first 2 years postdiagnosis were evaluated using mixed effects models, adjusting for age at diagnosis and baseline C-peptide. Results Adjusted slopes of AUC C-peptide decline did not differ between TEDDY subjects and community controls (P = 0.21), although the former had higher C-peptide baseline levels. In univariate analyses combining both groups (n = 113), younger age, higher weight and body mass index z-scores, female sex, an increased number increased number of islet autoantibodies, and IA-2A or ZnT8A positivity at baseline were associated with a higher rate of C-peptide loss. Younger age, female sex, and higher weight z-score remained significant in multivariate analysis (all P < 0.02). At 3 months after diagnosis, higher HbA1c became an additional independent factor associated with a higher rate of C-peptide decline (P < 0.01). Conclusion Younger age at diagnosis, female sex, higher weight z-score, and HbA1c were associated with a higher rate of C-peptide decline after T1D diagnosis in young children.

Objective: The Environmental Determinants of Diabetes in the Young (TEDDY) study is uniquely capable of investigating age-specific differences associated with type 1 diabetes. As age is a primary driver of heterogeneity in type 1 diabetes, we sought to characterize by age, metabolic derangements prior to diagnosis and clinical features associated with diabetic ketoacidosis (DKA). Research Design and Methods: The 379 TEDDY children who developed type 1 diabetes were grouped by age at onset (0-4, 5-9, and 10-14 years; n=142, 151 and 86, respectively) with comparisons of autoantibody profiles, HLA, family history of diabetes, presence of DKA, symptomatology at onset, and adherence to TEDDY protocol. Time-varying analysis of those with OGTT data was compared to TEDDY children who did not progress to diabetes. Results: Increasing fasting glucose (HR=1.09 (95% CI 1.04-1.14), p=0.0003), stimulated glucose (HR=1.50 (1.42-1.59), p Conclusions: DKA at onset in TEDDY is uncommon, especially for FDRs. For those without familial risk, metabolic monitoring continues to provide a primary benefit of reduced DKA but requires regular follow-up. Clinical and laboratory features vary by age at onset adding to the heterogeneity of type 1 diabetes.

Background Participants’ study satisfaction is important for both compliance with study protocols and retention, but research on parent study satisfaction is rare. This study sought to identify factors associated with parent study satisfaction in The Environmental Determinants of Diabetes in the Young (TEDDY) study, a longitudinal, multinational (US, Finland, Germany, Sweden) study of children at risk for type 1 diabetes. The role of staff consistency to parent study satisfaction was a particular focus. Methods Parent study satisfaction was measured by questionnaire at child-age 15 months (5579 mothers, 4942 fathers) and child-age four years (4010 mothers, 3411 fathers). Multiple linear regression analyses were used to identify sociodemographic factors, parental characteristics, and study variables associated with parent study satisfaction at both time points. Results Parent study satisfaction was highest in Sweden and the US, compared to Finland. Parents who had an accurate perception of their child’s type 1 diabetes risk and those who believed they can do something to prevent type 1 diabetes were more satisfied. More educated parents and those with higher depression scores had lower study satisfaction scores. After adjusting for these factors, greater study staff change frequency was associated with lower study satisfaction in European parents (mothers at child-age 15 months: − 0.30,95% Cl − 0.36, − 0.24, p p p p Conclusions Sociodemographic factors, parental characteristics, and study-related variables were all related to parent study satisfaction. Those that are potentially modifiable are of particular interest as possible targets of future efforts to improve parent study satisfaction. Three such factors were identified: parent accuracy about the child’s type 1 diabetes risk, parent beliefs that something can be done to reduce the child’s risk, and study staff consistency. However, staff consistency was important only for European parents. Trial registration NCT00279318.

The objective of this study was to explore whether recombinant GAD65 conjugated hydroxide (GAD-alum) treatment affected peripheral blood T-cell subpopulations in healthy children with multiple beta cell autoantibodies.The Diabetes Prevention-Immune Tolerance 2 (DiAPREV-IT 2) clinical trial enrolled 26 children between 4 and 13 years of age, positive for glutamic acid decarboxylase autoantibody (GADA) and at least one other autoantibody (insulin, insulinoma antigen-2, or zinc transporter 8 autoantibody (IAA, IA-2A, or ZnT8A)) at baseline. The children were randomized to two doses of subcutaneously administered GAD-alum treatment or placebo, 30 days apart. Complete blood count (CBC) and immunophenotyping of T-cell subpopulations by flow cytometry were performed regularly during the 24 months of follow-up posttreatment. Cross-sectional analyses were performed comparing lymphocyte and T-cell subpopulations between GAD-alum and placebo-treated subjects.GAD-alum-treated children had lower levels of lymphocytes (10Our findings suggest that levels of total T-cells and T-cell subpopulations declined 18 and 24 months after GAD-alum treatment in healthy children with multiple beta-cell autoantibodies including GADA.

The clinical diagnosis of new-onset type 1 diabetes has, for many years, been considered relatively straightforward. Recently, however, there is increasing awareness that within this single clinical phenotype exists considerable heterogeneity: disease onset spans the complete age range; genetic susceptibility is complex; rates of progression differ markedly, as does insulin secretory capacity; and complication rates, glycemic control, and therapeutic intervention efficacy vary widely. Mechanistic and immunopathological studies typically show considerable patchiness across subjects, undermining conclusions regarding disease pathways. Without better understanding, type 1 diabetes heterogeneity represents a major barrier both to deciphering pathogenesis and to the translational effort of designing, conducting, and interpreting clinical trials of disease-modifying agents. This realization comes during a period of unprecedented change in clinical medicine, with increasing emphasis on greater individualization and precision. For complex disorders such as type 1 diabetes, the option of maintaining the “single disease” approach appears untenable, as does the notion of individualizing each single patient’s care, obliging us to conceptualize type 1 diabetes less in terms of phenotypes (observable characteristics) and more in terms of disease endotypes (underlying biological mechanisms). Here, we provide our view on an approach to dissect heterogeneity in type 1 diabetes. Using lessons from other diseases and the data gathered to date, we aim to delineate a roadmap through which the field can incorporate the endotype concept into laboratory and clinical practice. We predict that such an effort will accelerate the implementation of precision medicine and has the potential for impact on our approach to translational research, trial design, and clinical management.

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. FUNDING: Juvenile Diabetes Research Foundation.

OBJECTIVE To use islet autoantibody titers to improve the estimation of future type 1 diabetes risk in children. RESEARCH DESIGN AND METHODS Prospective cohort studies in Finland, Germany, Sweden, and the U.S. followed 24,662 children at increased genetic or familial risk to develop islet autoimmunity and diabetes. For 1,604 children with confirmed positivity, titers of autoantibodies against insulin (IAA), GAD antibodies (GADA), and insulinoma-associated antigen 2 (IA-2A) were harmonized for diabetes risk analyses. RESULTS Survival analysis from time of confirmed positivity revealed markedly different 5-year diabetes risks associated with IAA (n = 909), GADA (n = 1,076), and IA-2A (n = 714), when stratified by quartiles of titer, ranging from 19% (GADA 1st quartile) to 60% (IA-2A 4th quartile). The minimum titer associated with a maximum difference in 5-year risk differed for each autoantibody, corresponding to the 58.6th, 52.4th, and 10.2nd percentile of children specifically positive for each of IAA, GADA, and IA-2A, respectively. Using these autoantibody type-specific titer thresholds in the 1,481 children with all autoantibodies tested, the 5-year risk conferred by single (n = 954) and multiple (n = 527) autoantibodies could be stratified from 6 to 75% (P < 0.0001). The thresholds effectively identified children with a ≥50% 5-year risk when considering age-specific autoantibody screening (57–65% positive predictive value and 56–74% sensitivity for ages 1–5 years). Multivariable analysis confirmed the significance of associations between the three autoantibody titers and diabetes risk, informing a childhood risk surveillance strategy. CONCLUSIONS This study defined islet autoantibody type-specific titer thresholds that significantly improved type 1 diabetes risk stratification in children.

Background: Participants’ study satisfaction is important for both study compliance and retention, but research on parental study satisfaction is rare. The aim of the study was to identify factors associated with parent study satisfaction in a longitudinal, multinational study of children at risk for type 1 diabetes, with a particular focus on the role of staff consistency.Methods: Mother and father study satisfaction was measured at child-age 15 months (5579 mothers and 4942 fathers) and child-age four years (4010 mothers and 3411 fathers). Multiple linear regression analyses were used to identify factors associated with study satisfaction at both time points.Results: Parent study satisfaction was highest in Sweden and the US compared to Finland. Parents with low education who had an accurate perception of their child’s type 1 diabetes risk and who believed they can do something to prevent type 1 diabetes were more satisfied with their study participation. Parents with higher depression scores had lower study satisfactions scores. After adjusting for these factors, study staff consistency was associated with greater study satisfaction in Europe but not the US. However, the number of staff changes from visit to visit was markedly higher in the US compared to Europe. Although study satisfaction scores were higher for mothers than fathers, associations with other factors were similar for both.Conclusions: Factors associated with parent study satisfaction were similar for mothers and fathers and across time. Potentially modifiable factors are of particular interest. These include accuracy about the child’s risk for type 1 diabetes; beliefs that something can be done to reduce the child’s risk; and study staff consistency. However, the importance of staff consistency was different between the US and European parents. Trial registration: NCT00279318

OBJECTIVE To assess the progression of type 1 diabetes using time to peak glucose or C-peptide during oral glucose tolerance tests (OGTTs) in autoantibody-positive relatives of people with type 1 diabetes. RESEARCH DESIGN AND METHODS We examined 2-h OGTTs of participants in the Diabetes Prevention Trial Type 1 (DPT-1) and TrialNet Pathway to Prevention (PTP) studies. We included 706 DPT-1 participants (mean ± SD age, 13.84 ± 9.53 years; BMI Z-score, 0.33 ± 1.07; 56.1% male) and 3,720 PTP participants (age, 16.01 ± 12.33 years; BMI Z-score, 0.66 ± 1.3; 49.7% male). Log-rank testing and Cox regression analyses with adjustments (age, sex, race, BMI Z-score, HOMA-insulin resistance, and peak glucose/C-peptide levels, respectively) were performed. RESULTS In each of DPT-1 and PTP, higher 5-year diabetes progression risk was seen in those with time to peak glucose >30 min and time to peak C-peptide >60 min (P < 0.001 for all groups), before and after adjustments. In models examining strength of association with diabetes development, associations were greater for time to peak C-peptide versus peak C-peptide value (DPT-1: χ2 = 25.76 vs. χ2 = 8.62; PTP: χ2 = 149.19 vs. χ2 = 79.98; all P < 0.001). Changes in the percentage of individuals with delayed glucose and/or C-peptide peaks were noted over time. CONCLUSIONS In two independent at-risk populations, we show that those with delayed OGTT peak times for glucose or C-peptide are at higher risk of diabetes development within 5 years, independent of peak levels. Moreover, time to peak C-peptide appears more predictive than the peak level, suggesting its potential use as a specific biomarker for diabetes progression.

Multiplex Antibody-Detection by Agglutination-PCR (ADAP) assay was compared to singleplex standard radiobinding assays (RBA) to detect autoantibodies against insulin (IAA), GAD65 (GADA), islet antigen-2 (IA-2A), ZnT8 (ZnT8A) and tissue transglutaminase (TGA). Serum samples from 273 (114F/158M), 15-73 years of age healthy controls and 227 (109F/118M) newly diagnosed type 1 diabetes children, 1-11 years of age, were analyzed in both assay systems.The original WHO standard 97/550 and in-house reference standards for RBA were compared to ADAP. The ADAP and RBA generated parallel reference standards in all assays except TGA. Lower detection limits were observed in the ADAP assay for GADA,IAA and ZnT8A, markedly for TGA, but not for IA-2A. The Receiver Operating Characteristics (ROC) curve AUC analyses for pairwise comparison of ADAP with RBA showed no difference for GADA (n.s.), ADAP greater AUC for IAA (p = 0.005), RBA greater AUC for IA-2A (p = 0.0004) and ZnT8A (p 0.0001) while ADAP TGA had a greater AUC compared to both RBA TGA-IgG (p 0.0001) and TGA-IgA (p 0.0001). These data suggest that the ADAP and RBA assays are comparable with equal performance for GADA, better ADAP performance for IAA while the RBA showed better performance in both IA-2A and ZnT8A associated with greater heterogeneity in autoantibody levels. The simultaneous analysis of 5 different autoantibodies by ADAP in sample volume reduced to only 4 μL and at an increased lower detection limit in all assays except IA-2A makes the ADAP automated autoantibody assay a distinct advantage for high throughput screening.

The TrialNet OI prevention trial (TN07) with T1D as an endpoint showed no effect. However, in a smaller trial run in parallel with TN07 (termed secondary stratum 1), in which all participants had low first-phase insulin release, a statistically significant delay in T1D onset was found. Since trials with T1D as an endpoint take years to complete, we aimed to determine if combination C-peptide and glucose markers could identify a therapeutic benefit after 12 months of OI in this high-risk population. All participants were at-risk relatives with multiple autoantibodies including mIAA and normal glucose tolerance (n=40 with full data). The Figure shows changes in glucose and C-peptide response curves and their centroids (central points). Glucose rose and C-peptide declined in the placebo group compared to a minimal glucose rise with an increase of C-peptide in the OI group. Change from baseline to 1 year in the centroid ratio (C-peptide/glucose) differed significantly between groups (p=0.037 after adjustments for age, BMI, and baseline C-peptide and glucose). The AUC C-peptide/AUC glucose ratio and Index60 did not differ (both p=0.108). In conclusion, the findings support a positive effect of OI on combined glucose and C-peptide endpoints in high-risk individuals. These measures appear to detect differences in a shorter time than the standard T1D endpoint. Disclosure L. M. Jacobsen: None. M. A. Atkinson: None. J. Krischer: None. D. Schatz: Advisory Panel; Self; Abbott, Medtronic. J. Sosenko: None. D. D. Cuthbertson: None. E. K. Sims: None. H. M. Ismail: None. L. Dimeglio: Advisory Panel; Self; MannKind Corporation. T. M. Triolo: None. M. J. Redondo: Advisory Panel; Self; Provention Bio, Inc. M. Lundgren: None. P. Gottlieb: Advisory Panel; Self; Janssen Research & Development, LLC, Tolerion, Inc., Viacyte, Inc., Other Relationship; Self; ImmunoMolecular Therapeutics, Inc., Research Support; Self; Caladrius Biosciences, Inc., Immune Tolerance Network, Mercia Pharma Inc., National Institute of Diabetes and Digestive and Kidney Diseases, Precigen, Inc., Tolerion, Inc.

Type 1 diabetes (T1D) is a chronic autoimmune disease that affects about 1 in 300 children and up to 1 in 100 adults during their life-time(1). Improvements in early prediction of T1D onset may help prevent diagnosis for diabetic ketoacidosis, a serious complication often associated with a missed or delayed T1D diagnosis. In addition to genetic factors, progression to T1D is strongly associated with immunologic factors that can be measured during clinical visits. We developed a T1D-specific ontology that captures the dynamic patterns of these biomarkers and used it together with a survival model, RankSvx, proposed in our prior work(2). We applied this approach to a T1D dataset harmonized from three birth cohort studies from the United States, Finland, and Sweden. Results show that the dynamic biomarker patterns captured in the proposed ontology are able to improve prediction performance (in concordance index) by 5.3%, 3.3%, 2.8%, and 1.0% over baseline for 3, 6, 9, and 12 month duration windows, respectively.

Islet autoantibodies are key markers for the diagnosis of type 1 diabetes. Since their discovery, they have also been recognized for their potential to identify at-risk individuals prior to symptoms. To date, risk prediction using autoantibodies has been based on autoantibody number; it has been robustly shown that nearly all multiple-autoantibody-positive individuals will progress to clinical disease. However, longitudinal studies have demonstrated that the rate of progression among multiple-autoantibody-positive individuals is highly heterogenous. Accurate prediction of the most rapidly progressing individuals is crucial for efficient and informative clinical trials and for identification of candidates most likely to benefit from disease modification. This is increasingly relevant with the recent success in delaying clinical disease in presymptomatic subjects using immunotherapy, and as the field moves toward population-based screening. There have been many studies investigating islet autoantibody characteristics for their predictive potential, beyond a simple categorical count. Predictive features that have emerged include molecular specifics, such as epitope targets and affinity; longitudinal patterns, such as changes in titer and autoantibody reversion; and sequence-dependent risk profiles specific to the autoantibody and the subject’s age. These insights are the outworking of decades of prospective cohort studies and international assay standardization efforts and will contribute to the granularity needed for more sensitive and specific preclinical staging. The aim of this review is to identify the dynamic and nuanced manifestations of autoantibodies in type 1 diabetes, and to highlight how these autoantibody features have the potential to improve study design of trials aiming to predict and prevent disease.

IntroductionThe Global Platform for the Prevention of Autoimmune Diabetes-SINT1A Study is designed as a randomised, placebo-controlled, double-blind, multicentre, multinational, primary prevention study aiming to assess whether daily administration of Bifidobacterium infantis from age 7 days to 6 weeks until age 12 months to children with elevated genetic risk for type 1 diabetes reduces the cumulative incidence of beta-cell autoantibodies in childhood.Methods and analysisInfants aged 7 days to 6 weeks from Germany, Poland, Belgium, UK and Sweden are eligible for study participation if they have a >10.0% expected risk for developing multiple beta-cell autoantibodies by age 6 years as determined by genetic risk score or family history and HLA genotype. Infants are randomised 1:1 to daily administration of B. infantis EVC001 or placebo until age 12 months and followed for a maximum of 5.5 years thereafter. The primary outcome is the development of persistent confirmed multiple beta-cell autoantibodies. Secondary outcomes are (1) Any persistent confirmed beta-cell autoantibody, defined as at least one confirmed autoantibody in two consecutive samples, including insulin autoantibodies, glutamic acid decarboxylase, islet tyrosine phosphatase 2 or zinc transporter 8, (2) Diabetes, (3) Transglutaminase autoantibodies associated with coeliac disease, (4) Respiratory infection rate in first year of life during supplementation and (5) Safety. Exploratory outcomes include allergy, antibody response to vaccines, alterations of the gut microbiome or blood metabolome, stool pH and calprotectin.Ethics and disseminationThe study was approved by the local ethical committees of the Technical University Munich, Medical Faculty, the Technische Universität Dresden, the Medizinische Hochschule Hannover, the Medical University of Warsaw, EC Research UZ Leuven and the Swedish ethical review authority. The results will be disseminated through peer-reviewed journals and conference presentations and will be openly shared after completion of the study.Trial registration numberNCT04769037.

Screening for pre-symptomatic T1D can significantly reduce the risk of DKA at diagnosis. Screening can also identify children who may benefit from interventions to delay/prevent progression from islet autoimmunity (IA) to diabetes. To help design a rational screening schedule, we analyzed data to determine the optimal ages to screen children for IA. Longitudinal data sets from birth cohort studies (DIPP, BABYDIAB, DiPiS, and DAISY) were harmonized for analyses. We analyzed the age of screening for multiple autoantibodies to insulin, GAD and IA-2 to maximize the sensitivity and positive predictive value (PPV) to predict development of T1D by age 15. Inverse probability censoring weighting was used to account for right censored outcomes. Cumulative sensitivity, dynamic specificity, positive and negative predictive values were used to describe the models. In the combined cohorts (n=6061), a one-time screening for multiple autoantibodies achieved a maximum sensitivity of 35% and a PPV of 70-80% when applied at age 3-5 yr. To increase the sensitivity above 50% and retain PPV >70%, two screenings should occur: at 2-3 yr and at 5-6 yr (Fig). Public health screening efforts to identify children progressing to T1D must make compromises based on feasibility, cost, optimal metrics. Our data represent a starting point for these considerations that should be customized based on the population’s underlying disease characteristics and public health infrastructure. Disclosure M. Ghalwash: None. V. Anand: None. W. Hagopian: Consultant; Self; Novo Nordisk Inc. M. Lundgren: None. M. Rewers: None. R. Veijola: None. A. Ziegler: None. J.L. Dunne: None.

Few studies have described the metabolic phenotype of AbPos individuals who remain T1D free for an extended period of time. We aimed to compare C-peptide and glucose (Glu) data from oral glucose tolerance tests (OGTTs) performed longitudinally in 646 AbPos TrialNet Pathway to Prevention (PTP) participants who remained T1D free for ≥5.0 yrs (LTNPs; mean follow-up: 7.8±2.3 yrs) versus 405 Rapid Progressor (RP) PTP participants who progressed to T1D within Disclosure C. Evans-Molina: Consultant; Self; Bristol-Myers Squibb. Z.I. Saeed: None. J. Sosenko: None. M.A. Atkinson: None. K.C. Herold: Consultant; Self; Provention Bio, Inc. H.M. Ismail: None. H. Elding Larsson: None. M. Lundgren: None. A. Moran: Research Support; Self; Abbott, Intrexon, JDRF, Medtronic, National Institute of Diabetes and Digestive and Kidney Diseases, Provention Bio, Inc. Other Relationship; Self; Novo Nordisk Inc. D.J. Moore: None. B.M. Nathan: None. J.P. Palmer: None. E.K. Sims: None. A. Steck: None. D.K. Wherrett: None. M.J. Redondo: None. Funding National Institutes of Health (U01107014, U01DK106993)

OBJECTIVE: Treatments have failed to delay or stop the autoimmune process, preceding onset of type 1 diabetes. We investigated if autoantigen-specific treatment with alum-formulated glutamate decarboxylase (GAD-Alum) was safe and affected progression to type 1 diabetes in children with islet autoimmunity.METHODS: In an investigator-initiated, double-blind, placebo-controlled clinical trial, non-diabetic children aged 4 to 17.9 years with autoantibodies to glutamate decarboxylase (GADA) and at least one of insulinoma-associated protein 2, insulin or zinc-transporter 8, were randomized, stratified by 2 or ≥3 islet autoantibodies, to 2 injections of 20 μg GAD-Alum or placebo, 30 days apart. Main outcome was safety, investigated by adverse events, hematology, chemistry, thyroid and celiac autoimmunity and titers of islet autoantibodies, and efficacy, investigated by cumulative incidence of diabetes onset over 5-year follow-up. Secondary variables: change in first-phase insulin release (FPIR) after intravenous glucose tolerance tests, fasting, 120 minutes and Area under the curve (AUC) C-peptide and p-glucose after oral glucose tolerance tests and HbA1c.RESULTS: Fifty children (median age: 5.2) were assigned 1:1 to GAD-Alum or placebo, all receiving full treatment and included in the analyses. GAD-Alum did not affect any safety parameter, while GADA titers increased (P = .001). Time to clinical diagnosis was not affected by treatment (hazard ratio, HR = 0.77, P = .574) in the full population or in the separate stratum groups. Treatment did not affect any of the secondary variables.CONCLUSIONS: GAD-Alum as a subcutaneous prime and boost injection was safe in prediabetic young children but did not affect progression to type 1 diabetes. The safety of GAD-Alum should prove useful in future prevention studies. (Less)

Aim: Parents of children participating in screening studies may experience increased levels of anxiety. The aim of this study was to assess parental anxiety levels after 5 years of participation in the Diabetes Prediction in Skane study. Associations between parental anxiety about their child developing type 1 diabetes and clinical, demographic, and immunological factors were analyzed. Method: Mothers and fathers of participating 5-year-old children answered a questionnaire regarding parental anxiety associated with their child's increased risk of type 1 diabetes. Anxiety levels were assessed using the State Anxiety Inventory scale. Data were analyzed using logistic and multinomial regression. Results: Parents of 2088 5-year-old children participated. Both parents answered the questionnaire for 91.2% (n = 1904) of children. In 67.1% of families, neither parent reported being anxious that their child had an increased risk of developing type 1 diabetes. Anxiety was higher in mothers of children positive for autoantibodies (OR 2.21 95% CI 1.41, 3.48, P

BACKGROUND: The concept of organ transplantation as treatment for complex genetic conditions, including Wolcott-Rallison syndrome (WRS), continues to show promise. Liver transplantation is essential for survival of patients with WRS, and pancreas transplantation cures their type I diabetes mellitus. METHODS: The recipient, a 3-year-old girl weighing 14 kg at the time of transplantation, suffered from major complications of WRS, including repetitive liver failure episodes and poorly controlled diabetes. The patient underwent a nonacute, combined, simultaneous liver and pancreas transplantation from a pediatric donor without using the en bloc technique. RESULTS: Well-preserved graft functions at 2-year follow-up with normal liver and pancreas function. CONCLUSIONS: This is the first case report of simultaneous liver and pancreas transplantation as treatment of WRS in a small child in Europe. Two-year follow-up demonstrates that organ transplantation can halt life-threating recurrent liver failure episodes and cure type 1 diabetes. (Less)

Four birth-cohort studies in the U.S. (DAISY, DEWIT), Sweden (DiPiS) and Finland (DIPP) were initiated in 1990s to early 2000. General population newborns and young first-degree relatives (FDR) of T1D patients, preselected on increased HLA risk, were followed for islet autoantibodies (AAb)and T1D. We harmonized their common variables, including AAb to insulin, IA-2, GAD, or ZnT8, HLA-DR-DQ risk genotypes, socio-demographics, diet, family history, glucose/HbA1c and anthropometric measures. The combined cohort included 22,312 subjects with AAb measured in 256,243 visits (mean 11±10 per subject) at 3-12 month intervals. During follow-up, 2279 (10%) subjects developed at least one AAb, 914 (4%) multiple AAb and 603 (3%) T1D. The cumulative risk of T1D differed by the number of AAb at seroconversion (Figure). T1D developed in 26% (95% CI 24-32%) of 1AAb, 62% (55-68%) of 2AAb and 84% (75-88%) of >=3AAb positive subjects in the 15y after seroconversion. T1D risk varied by HLA genotype: 40% (35-48%) in DR3/4-DQ2/8, 22% (18-31%) in DR4/x-DQ8/x and 21% (18-35%) in DR3/x-DQ2/x of 1AAb subjects and, respectively, in 82% (68-88%), 61%(50-65%)and 87% (32-98%)of 2AAb subjects. Once ≥2 AAb positive, HLA genotype was less predictive of progression to T1D. This harmonized dataset offers an unparalleled opportunity to validate and improve upon reported risk estimates for T1D development via novel analytical methods. Disclosure V. Anand: None. B. Liu: None. M. Ghalwash: None. E. Koski: None. K. Ng: None. J.L. Dunne: None. W. Hagopian: Research Support; Self; Novo Nordisk A/S. M. Lundgren: None. Å. Lernmark: None. J. Ilonen: None. R. Veijola: None. M. Rewers: None. Funding JDRF

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 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

Progression to type 1 diabetes (T1D) is strongly predicted by multiple risk factors including genetic and non-genetic variables such as islet autoantibodies (AAb). Little is known, however, about the impact of interactions among the known risk factors on T1D risk prediction. Through consideration of interactions among risk factors, we aimed to evaluate time-to-diagnosis for T1D from seroconversion for AAb using a novel time-to-event model (RankSvx). RankSvx simultaneously predicts subject-level event times and risk scores as measured by Mean Absolute Error (MAE) and Concordance Index (CI), respectively. We control for subject-level characteristics (4 HLA groups, sex, and their interactions with AAb, age at seroconversion and visit-level AAb count) and use LASSO for feature selection. We also explored up to 12 months of AAb dynamics (i.e., appearance or disappearance of AAb in 3-month intervals) post seroconversion using an interaction model and compared it to a non-interaction (baseline model at seroconversion) and a traditional Cox model. We used a harmonized dataset of the DAISY, DiPiS, and DIPP studies with 1,880 AAb positive subjects of whom 512 (27%) progressed to T1D. First, we found the interaction model was superior to the baseline model without interactions (Wilcoxon Rank test p-value: 0.016). Second, incorporating AAb dynamics further improved T1D onset time prediction. When including post seroconversion AAb dynamics at 12 months, the interaction model performed 15% better than the baseline model (CI: 0.83 vs. 0.68, MAE: 2.5 years). Finally, the top identified interactions included: 1) young age at seroconversion interaction with IA-2A, and 2) HLA Group that includes at least one DR3-DQ2.5 haplotype interaction with ZnT8A or GADA. Our results are consistent with recent findings in the TEDDY dataset of patterns of AAb appearance and genetic association and age-related T1D incidence. Further investigations on seroconversion and post seroconversion endotypes are necessary. Disclosure B. Liu: None. V. Anand: None. M. Ghalwash: None. K. Ng: None. J.L. Dunne: None. R. Veijola: None. M. Rewers: None. M. Lundgren: None. Funding JDRF

People with type 1 diabetes (T1D) are at increased risk for comorbid autoimmune disease (CAID). The TrialNet Pathway to Prevention (PTP) Study screens relatives of people with T1D for islet autoantibodies (Ab) to determine T1D risk. PTP Ab+ participants self-reported (Y vs. N) whether or not they had CAIDs at the start of monitoring. Outside of celiac disease, CAID has not been examined in Ab+ participants. Of the 5,740 Ab+ participants, 586 (10.2%; 95% CI: 9.4 - 11.0%) reported having a CAID. Of those with a single confirmed Ab+, GADA+ participants had a higher prevalence of CAID (230/1709=13.5%) vs. those who were IA-2A+ (8/88=9.1%) or mIAA+ (40/592=6.8%; p In conclusion, the reported prevalence rates of CAID is high in Ab+ relatives of people with T1D, inversely correlated with the number of Ab present, and related to DR3 and DR4/DQ8 haplotypes. The change in the direction of association of CAID with Ab number after an adjustment for HLA status suggests that DR3 and DR4/DQ8 could contribute to both CAID and Ab development. Disclosure K. Simmons: None. S. Geyer: None. D.K. Wherrett: None. H.M. Ismail: None. M. Lundgren: None. L.E. Bocchino: None. J.M. Sosenko: None. C. Greenbaum: Research Support; Self; Janssen Research & Development. A. Steck: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (5U01DK0855095, K12DK094712-08)

Clinical type 1 diabetes (T1D) is preceded by asymptomatic islet-autoimmunity (IA) with presence of islet autoantibodies (AAb). However, both AAb profiles (clusters) during IA phase and progression rate to T1D are heterogeneous. It is unclear if AAb profiles affect the progression to T1D similarly in different countries. We analyzed data from 3 prospective birth cohorts, DAISY (U.S.), DiPiS (Sweden), and DIPP (Finland), to address this question. We developed a similarity algorithm that considers subject-level temporal IA profiles (based on 4 AAbs - IAA, GADA, IA2A, and ZnT8A positivity or negativity), age at which AAb developed, and imbalance in AAb positivity by weighting positive AAb matches higher than negative ones. Using this subject-level similarity measure, we performed hierarchical clustering of AAb positive subjects having at least 3 visits. We used a log-rank test to identify the number of clusters in 1125 DIPP subjects (329 T1D cases), 309 DAISY subjects (70 cases), and 226 DiPiS subjects (33 cases). Each dataset was individually clustered. We identified three clusters in each dataset associated with different rate of progression from seroconversion to T1D - slow progressors (SP), moderate progressors (MP) and fast progressors (FP). These were distributed as follows: DIPP (654 SP with 3% 10-year T1D risk, 356 MP with 60% risk, 115 FP with 96% risk); DAISY (215 SP with 6% 10-year risk, 92 MP with 62% risk, 2 FP with 100% risk); and DiPiS (160 SP with 3% 10-year risk, 62 MP with 58% risk, 4 FP with 100% risk). Finally, we tested the generalizability of the clusters by 1) learning a clustering from one data set “learned clusters,” 2) classifying subjects from another (independent) data set with the “learned clusters,” and 3) measuring how often the subjects were assigned to the same cluster (SP, MP, FP) as the “internal” clustering. The average accuracy of cluster transfer was high (mean = 87.9%, sd = 1.1%) suggesting that the IA profile predicts progression rate to T1D independently of the population. Disclosure M. Ghalwash: None. V. Anand: None. B. Liu: None. E. Koski: None. K. Ng: None. J.L. Dunne: None. M. Lundgren: None. M. Rewers: None. R. Veijola: None. Funding JDRF

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups., Comment: to appear at IEEE Transactions on Visualization and Computer Graphics

The risk for type 1 diabetes is strongly associated with HLA-DQ and the appearance of beta cell autoantibodies against either insulin, glutamate decarboxylase (GAD65), insulinoma-associated protein-2 (IA-2), or zinc transporter 8 (ZnT8). Prolonged exposure to autoantibodies may be related to T cell exhaustion known to occur in chronic infections or autoimmune disorders. It was hypothesized that autoantibody exposure may affect HLA-DQ expression on peripheral blood cells and thereby contribute to T cell exhaustion thought to be associated with the pathogenesis of type 1 diabetes. The aim of this study was to determine whether autoantibody exposure as an expression of autoimmunity burden was related to peripheral blood cell HLA-DQ cell surface expression in either 1) a cross-sectional analysis or 2) cumulative as area under the trajectory of autoantibodies during long term follow-up in the Diabetes Prediction in Skåne (DiPiS) study. Children (n = 67), aged 10–15 years were analyzed for complete blood count, HLA-DQ cell surface median fluorescence intensity (MFI), autoantibody frequency, and HLA genotypes by Next Generation Sequencing. Decreased HLA-DQ cell surface MFI with an increasing number of autoantibodies was observed in CD16+, CD14+CD16−, CD4+ and CD8+ cells but not in CD19+ cells and neutrophils. HLA-DQ cell surface MFI was associated with HLA-DQ2/8 in CD4+ T cells, marginally in CD14+CD16− monocytes and CD8+ T cells. These associations appeared to be related to autoimmunity burden. The results suggest that HLA-DQ cell surface expression was related to HLA and autoimmunity burden., Highlights • PBMC HLA-DQ surface expression in beta cell autoimmunity is poorly understood. • Children, 10–15 years of age without or with beta cell autoantibodies were analyzed. • HLA-DQ cell surface expression decreased with increasing number of autoantibodies. • HLA-DQ cell surface expression was related to HLA and autoimmunity burden.

Aims/hypothesis: It has been shown that children previously enrolled in follow-up studies have better glycaemic control during the early period after diabetes diagnosis. The aim of this study was to analyse glycaemic control over a longer period, past the period of partial remission, after diagnosis in children followed before diagnosis in the Swedish Diabetes Prediction in Skane (DiPiS) study compared with children of equal age not enrolled in pre-diabetes follow-up, receiving equivalent diabetes care. Methods: HbA1c from diagnosis and for the following 5 years, as well as differences in insulin dosage, BMI, pump use, partial remission according to insulin dose-adjusted HbA1c and baseline demographics were compared between children who were enrolled in follow-up and had received information on diabetes risk (n = 51) and children not enrolled in follow-up (n = 78). Results: The group followed before diagnosis had a higher proportion of first-degree relatives (FDRs) with diabetes (28% vs 5.6%; p = 0.001) and a higher proportion of participants with mothers born in Sweden (100% vs 89%; p = 0.02). No significant differences in total daily insulin dose, pump use or other baseline sociodemographic factors were detected between the groups. Median HbA1c at diagnosis and at 1, 2, 3, 4 and 5 years after diabetes diagnosis was significantly lower in children followed before diagnosis (all p < 0.05), and was not related to FDR status. Conclusions/interpretation: Compared with controls not previously enrolled in follow-up, our study shows that children enrolled in longitudinal follow-up before the diagnosis of diabetes have better glycaemic control, measured by HbA1c, up to 5 years after diagnosis and during the initial period of partial remission. Improved glycaemic control in the initial years of living with type 1 diabetes could affect long-term outcome and complications and might also improve study enrolment in future longitudinal studies. (Less)

Aims Stress and severe life events (SLEs) modify autoimmune disease susceptibility. Here, we aimed to establish if SLEs reported by parents during the first 2 years of life influence the risk of developing type 1 diabetes (T1D) using data from the prospective Diabetes Prediction in Skåne (DiPiS) study. Methods Prospective questionnaire data recorded at 2 months (n = 23,187) and 2 years of age (n = 3784) from the DiPiS cohort of children were included in the analysis. SLEs were analyzed both by groups and as a combined variable. A Cox proportional hazards model was used to calculate hazard ratios (HRs) for T1D diagnosis for the total cohort and for the HLA-DQ2/8 high-risk population. Affected first-degree relatives, HLA-DQ risk group, paternal education level, and parents’ country of birth were included as covariates. Results There was a significantly increased risk of T1D in children with SLEs occurring during the child’s first 2 years of life for both the total cohort (HR 1.67; 95% CI 1.1, 2.7; p = 0.03) and the DQ2/8 cohort (HR 2.2; 95% CI 1.1, 4.2; p = 0.018). Subgroup analysis of events related to unemployment, divorce, or family conflict showed a significant hazard for these events occurring both during and after pregnancy in the DQ2/8 cohort (HR 2.17; 95% CI 1.1, 4.3; p = 0.03 and HR 4.98; 95% CI 2.3, 11; p

Aims/hypothesis: Children participating in longitudinal type 1 diabetes prediction studies were reported to have less severe disease at diabetes diagnosis. Our aim was to investigate children who from birth participated in the Diabetes Prediction in Skane (DiPiS) study for metabolic status at diagnosis and then continued to be followed for 2 yr of regular clinical care. Methods: Children, followed in DiPiS before diagnosis, were compared to children in the same birth cohort, who did not participate in follow-up. Metabolic status, symptoms at diagnosis as well as hemoglobin A1c (HbA1c) and doses of insulin at 3, 6, 12, and 24 months after diagnosis were compared. Results: Children, followed in DiPiS and diagnosed at 2-12 yr of age, had 0.8% (9 mmol/mol) lower HbA1c at diagnosis than those who were not followed (p=0.006). At diagnosis, fewer DiPiS children had symptoms (p=0.014) and ketoacidosis at diagnosis were reduced (2% compared to 18%, p=0.005). During regular clinical care, HbA1c levels for the DiPiS children remained lower both at 12 (0.4% (4 mmol/mol); p=0.009) and 24 months (0.8% (9 mmol/mol) p < 0.001) after diagnosis, despite no difference in total daily insulin between the two groups. Conclusions: Participation in prospective follow-up before diagnosis of type 1 diabetes leads to earlier diagnosis with fewer symptoms, decreased incidence of ketoacidosis as well as better metabolic control up to 2 yr after diagnosis. Our data indicate that metabolic control at the time of diabetes diagnosis is important for early metabolic control possibly affecting the risk of long-term complications. (Less)

In this paper, we map the distribution of scale-rayed wrasse Acantholabrus palloni in eastern Skagerrak based on a combination of verified and personally communicated angling records. Long thought to be occasional vagrants outside its known range in the eastern Atlantic Ocean and Mediterranean Sea, we ask if this rare and understudied labrid has expanded its range and become established in Swedish waters. A recent surge in verified angling records in the Swedish Anglers Association’s specimen database Storfiskregistret provides information to suggest that this species should no longer be considered an occasional guest, but rather a species established in the Swedish parts of Skagerrak. These records are supported by additional personal communications with anglers. The species is currently well spread geographically along the Swedish Skagerrak coast, with many locations providing repeated captures of adult fish over multiple years. The typical Swedish catch sites are rocky reefs located between the general 40- and 80-m depth curves, likely influenced by currents bringing higher-salinity water from the North Sea. The present study show that angling records can provide an important, but underutilized, resource for mapping the distribution of data-deficient fish species.

BACKGROUND & AIMS: It is not clear how intake of gluten during infancy affects subsequent risk of celiac disease. We investigated whether gluten intake before 2 years of age increases risk for celiac disease in genetically susceptible children. METHODS: We performed a case-control study of 436 pairs of children, generated from a database of 2525 children with genetic susceptibility to celiac disease in Sweden, matched for sex, birth year, and HLA genotype from September 2004 and February 2010. Children were screened annually for celiac disease using an assay for tissue transglutaminase autoantibodies (tTGA). Intestinal biopsies were collected from children who tested positive for tTGA to confirm the presence of celiac disease. Gluten intake was calculated from 3-day food records collected when the children were 9, 12, 18 and 24 months old. RESULTS: Breastfeeding duration (median 32 weeks) and age at first introduction to gluten (median 22 weeks) did not differ between cases and tTGA-negative children (controls). At the visit prior to tTGA seroconversion, cases reported a larger intake of gluten (median 4.9 g/day) than controls (median 3.9 g/day) (odds ratio [OR], 1.28; 95% confidence interval [CI], 1.13-1.46; P=.0002). More cases consumed amounts of gluten in the upper 3rd tertile (i.e. >5.0 g/day) before they tested positive for tTGA seroconversion than controls (OR, 2.65; 95% CI, 1.70-4.13; P