Introduction and Objective: Exocrine pancreas dysfunction is evident in presymptomatic type 1 diabetes (T1D), yet its natural history remains unclear. We evaluated whether longitudinal decline in fecal elastase (FE-1) tracks more closely with islet autoimmunity or progression to dysglycemia and assessed its predictive utility. Methods: In a nested case-control study within The Environmental Determinants of Diabetes in the Young (TEDDY), longitudinal FE-1 was measured in two cohorts: Cohort A (n=122; 19 samples/subject) who progressed to Stage 3 T1D by age 4, and Cohort B (n=104; 23 samples/subject) who developed Stage 1 T1D by age 4 without dysglycemia by age 6, each with matched controls. FE-1 was compared cross-sectionally at baseline (median age 4 months), Stage 1, and Stage 3 using conditional logistic regression and paired Wilcoxon tests. Linear mixed models assessed log-transformed FE-1 trajectories over time, centered at Stage 1 onset (both cohorts) and Stage 3 onset (Cohort A), adjusting for age, sex, site, and HLA. ROC analyses evaluated absolute and rate of FE-1 decline at Stage 1 as predictors of Stage 3 progression. Results: Cohort A cases had lower FE-1 than controls at baseline (1241 vs 1586 μg/g, p=0.004), Stage 1 (944 vs 1238 μg/g, p=0.006), and Stage 3 (533 vs 1383 μg/g, p<0.001), while Cohort B showed no case-control differences. FE-1 declined prior to Stage 1 and 3 in Cohort A (β=-0.0006, p<0.001) and Stage 1 in Cohort B (β=-0.0003, p=0.001), with a steeper decline in Cohort A (p<0.001). GAD-first autoimmunity was associated with greater FE-1 decline than MIAA-first prior to Stage 1 in both cohorts and Stage 3 in Cohort A. FE-1 alone demonstrated poor-to-fair discrimination (AUC 0.57-0.62); however, combining FE-1 with first appearing autoantibody improved prediction of Stage 3 progression (AUC 0.71 vs 0.64 with first autoantibody alone). Conclusion: Early and progressive FE-1 decline identifies children at risk for early progression to clinical T1D and supports integration of exocrine biomarkers into prevention-stage risk stratification. Disclosure B.S. Bruggeman: Research Support; Current; GentiBio. K. Morneault-Gill: None. C. Wasserfall: None. H. Gao: None. M. Guyot: None. C.E. Forsmark: Consultant; Current; AbbVie Inc., Nestlé Health Science. D. Schatz: Research Support; Current; Immune Tolerance Network. Consultant; Current; Kriya Therapeutics. Research Support; Current; National Institute of Diabetes and Digestive and Kidney Diseases. Consultant; Current; Biomea, Amarna. Advisory Panel; Current; PoITREG. J. Krischer: None. M.L. Campbell-Thompson: None. M.J. Haller: Advisory Panel; Current; MannKind Corporation, Sanofi, SAB Biotherapeutics, Inc. Funding National Institutes of Health (R03DK129971, K23DK131363)

Objective The TrialNet Oral Insulin Prevention Trial (TN07) tested oral insulin to prevent Stage 3 type 1 diabetes in 560 Stage 1 relatives of individuals with type 1 diabetes (T1D). Of the three pre-defined risk strata, participants in Secondary Stratum 1 (SS1), characterized by low first-phase insulin release (n=55), responded significantly better to oral insulin. We aimed to identify genetic factors associated with treatment response. Research Design and Methods The TEDDY-T1DExomeChip was used to genotype 552 participants with available DNA. Cox models examined associations between response to oral insulin and HLA haplotypes, 33 pre-selected T1D-associated SNPs, the T1D genetic risk score-2 (T1D-GRS2), and type 2 diabetes (T2D)-associated polygenic scores. For primary analyses, p-values were Benjamini-Hochberg (BH)-corrected for multiple comparisons; results not passing correction were considered nominal. Results GLIS3 rs7020673 was significantly associated with response to oral insulin in SS1 (BH-corrected p-value=0.031 without and p=0.022 with covariate adjustment). Additional nominal associations included better response with HLA-DRB1*04:01-DQA1*03:01-DQB1*03:02 (HR=0.22 vs HR=1.09; unadjusted/adjusted p=0.031/0.045) in SS1, and worse responses with TNFAIP3 and CTLA4 in at least one stratum. In exploratory analyses, participants with T1D-GRS2 >12.5 responded better to oral insulin (HR=0.68) than those with T1D-GRS2 ≤12.5 (HR=2.10; unadjusted/adjusted p=0.003/0.006) in the overall cohort, and lower proinsulin- and obesity-partitioned T2D polygenic scores were associated with greater treatment benefit in SS1 and in another secondary stratum, respectively. Conclusions Genetic differences distinguish responders from non-responders to oral insulin for T1D prevention. Genetics may enable precision medicine by identifying individuals likely to benefit from T1D-modifying therapies.

Introduction and Objective: In population screening for islet autoantibodies (IAbs) and type 1 diabetes (T1D) risk, most individuals tested ‘positive’ have only one detectable IAb. Single IAb by standard radio binding assay (RBA) has limited prediction for progression to stage 3 T1D. Combining electrochemiluminescence (ECL) antibody assays with T1D genetic risk score (GRS) may improve prediction. This study examined ECL IAbs, T1D GRS2, and their combined predictive value in TrialNet participants with a single IAb. Methods: We analyzed first available IAb-positive samples from 853 TrialNet screening study participants who tested positive by RBA for a single IAb confirmed across ≥2 consistent visits (575 with GADA, 236 with IAA, 42 with IA-2A). The samples were blindly tested with ECL IAb assays. All participants were previously genotyped using the TEDDY array (GWAS with exome and custom content) and had T1D GRS2 scores determined. Results: Of those with single IAb by RBA, 323/853 (37.9%) were confirmed with ECL assays, showing a significantly higher 5-year cumulative incidence of stage 3 T1D (14.7% vs. 2.2%, p <0.001) and higher T1D GRS2 scores (12.9 ± 2.4 vs. 11.8 ± 2.3, p <0.001) than those not confirmed by ECL assays. ECL-GADA was significantly correlated with T1D GRS2 for both positivity (r=0.26) and levels (r=0.18, both p <0.0001). Both ECL IAb positivity (HR 8.0, 95% CI 3.7-17.6) and T1D GRS2 (HR 1.3, 95% CI 1.1-1.6) independently predicted progression to stage 3 T1D after adjusting for age, sex and BMI. Using ROC AUC as a metric, combining ECL IAb and T1D GRS2 improved 5-year prediction of stage 3 T1D to AUC 0.816 compared to ECL IAb (AUC 0.806, n.s.) or GRS2 (AUC 0.743, p <0.01) alone. Conclusion: In summary, both ECL-detected IAbs and T1D GRS independently enhance the disease prediction among single IAb positive individuals. Their combination further improves prediction, supporting an integrated approach for improved risk stratification in early-stage T1D screening strategies. Disclosure X. Jia: None. C. Zhang: None. A. Steck: Consultant; Current; Sanofi. R.A. Oram: Other - Research support, The University of Exeter has a licensing and royalty agreement for a 10 SNP T1D GRS with Randox; Current; Randox. Other - Advisory Panel, Consulting, Invited talks; Current; Sanofi. Other - invited talk, internal teaching talk on genetics; Ended; Novo Nordisk. D. Cuthbertson: None. H. Parikh: None. S. Onengut-Gumuscu: None. S. Rich: Advisory Panel; Current; Sanofi. Research Support; Current; Sanofi, Leona M. and Harry B. Helmsley Charitable Trust. Other - Consulting Associate Editor, Diabetes Care; Current; American Diabetes Association. J. Krischer: None. P. Gottlieb: Consultant; Current; Eli Lilly and Company. Board Member; Current; IM Therapeutics. Other - CEO, CMO; Current; IM Therapeutics. Research Support; Current; Immune Tolerance Network, Nova Laboratories, National Institute of Diabetes and Digestive and Kidney Diseases. Advisory Panel; Current; Sanofi. Research Support; Current; Sanofi. Consultant; Current; SAB Biotherapeutics, Inc., Anaptys Bio, Cour, T1D Fund. Consultant; Ended; Imcyse, Viacyte, Abata. M. Redondo: Advisory Panel; Current; Sanofi. Other - Data Safety Monitoring committee; Current; Lilly. L. Yu: None. Funding Colorado Diabetes Research Center (DRC) Pilot and Feasibility (P&F) program

Introduction and Objective: C-peptide is the most reliable biomarker of β-cell function in type 1 diabetes (T1D) and is strongly associated with glycemic control and risk of complications. We propose estimate of time to minimum residual C-peptide (TMRCP), which uses all available longitudinal data, to serve as an efficacy measure in T1D trials. Methods: TMRCP is defined as the time until C-peptide levels decline to a prespecified value representing functional loss of β-cell secretory capacity. We estimate it using a nonlinear mixed-effects model applied to log-transformed mean area under curve (MAUC) C-peptide values from mixed-meal tolerance testing. This approach makes efficient use of all available longitudinal measurements, appropriately accounts for repeated observations within individuals and variable visit schedules or incomplete follow-up, and provides subject-specific TMRCP estimates. Results: We applied the TMRCP estimation framework to six randomized, placebo-controlled, double-blind trials conducted by TrialNet in recent-onset T1D patients (total N=597). Using Wilcoxon rank-sum test on TMRCP estimates, we identified the same three treatments as effective as the original analyses. Compared to the corresponding placebo group, median TMRCP for the three effective treatments were 128 vs 84 days for Rituximab (p=0.034), 92 vs 70 days for Abatacept (p=0.030), and 110 vs 53 days for anti-thymocyte globulin (p=0.027), while the differences were less than 4 days for the three non-effective treatments (mycophenolate mofetil and daclizumab combined therapy, glutamic acid decarboxylase, and canakinumab). Conclusion: Unlike fixed-time endpoints, TMRCP leverages full longitudinal data to measure loss of residual insulin secretion and provides subject-specific estimates that support robust treatment comparisons. We conclude that TMRCP is a clinically interpretable and statistically efficient endpoint for future T1D intervention studies. Disclosure A.A. Ding: None. J. Krischer: None. H. Gao: None. H. Rodriguez: Advisory Panel; Ended; MannKind Corporation. Research Support; Current; Sanofi. Speaker's Bureau; Ended; Sanofi. Research Support; Ended; MannKind Corporation. Research Support; Current; Lilly. Other - DSMB; Ended; Merck Sharp & Dohme Corp. Other - DSMB; Current; Sanofi. Research Support; Ended; Novo Nordisk. Research Support; Current; Zucara Therapeutics. Research Support; Ended; Dexcom, Inc. Research Support; Current; Cour Pharma. Research Support; Ended; MannKind Corporation. S.S. Wu: Consultant; Current; Vertex Pharmaceuticals Incorporated. Stock/Shareholder; Current; United HealthCare Services, Inc. Funding National Institutes of Health (5U01 DK106993)

Context: The influence of genetic factors on the transition through preclinical stages of type 1 diabetes (T1D) has not been studied. Objective: Our aim was to evaluate the influence of genetic factors on transition through T1D stages. Methods: In TrialNet participants who have been genotyped with the TEDDY-T1DExomeChip array (Illumina HumanCoreExome Beadarray with custom content), we evaluated the influence of the overall T1D genetic risk score (GRS2), its human leukocyte antigen (HLA) and non-HLA components, HLA-DR3 and HLA-DR4 haplotypes, and 90 single-nucleotide variations previously associated with islet autoimmunity and/or T1D on 3 transitions between diabetes stages: from single confirmed autoantibody positive to stage 1 (N = 4314), from stage 1 to stage 2 (N = 3066), and from stage 2 to stage 3 (clinical) T1D (N = 2045). Results: The T1D GRS2 was associated with all 3 transitions with hazard ratios (HRs) of 1.11 (1.09-1.14) for single-autoantibody positivity to stage 1, HR 1.05 (1.03-1.08) for stage 1 to 2, and HR 1.13 (1.09-1.17) for stage 2 to 3 T1D. The T1D GRS2 HLA and HLA class II components were associated with all 3 transitions. The HLA class I component and the HLA-DR4 haplotype were associated with the transition from single-autoantibody positivity to stage 1 and from stage 2 to stage 3 T1D, while HLA-DR3 was associated only with the latter. Conclusion: Genetics influence transitions through each stage of preclinical T1D, with main contributions from HLA class II. These results increase our understanding of T1D development and support incorporating the T1D GRS2 to enhance the prediction of progression through the preclinical stages of T1D.

The appearance of diabetes-associated autoantibodies is the first detectable sign of the disease process leading to type 1 diabetes (T1D). Evidence suggests that T1D is a heterogenous disease, where the type of antibodies first formed imply subtypes. Here, we followed 49 children, who subsequently presented with T1D and 49 matched controls, profiling single-cell epigenomics at different time points of disease development. Quantitation of cell and nuclei populations as well as transcriptome and open-chromatin states indicated robust, early, replicable monocyte lineage differences between cases and controls, suggesting heightened pro-inflammatory cytokine secretion early among cases. The order of autoantibody emergence in cases showed variation across lymphoid and myeloid cells, potentially indicating cellular immune response divergence. The strong monocytic lineage representation in peripheral blood immune cells before seroconversion and the weaker differential coordination of these gene networks close to clinical diagnosis emphasizes the importance of early life as a critical phase in T1D development.

Efficient prediction of clinical type 1 diabetes is important for risk stratification and monitoring of autoantibody-positive individuals. In this study, we compared type 1 diabetes predictive models for predictive performance, cost and participant time needed for testing. We developed 1943 predictive models using a Cox model based on a type 1 diabetes genetic risk score (GRS2), autoantibody count and types, BMI, age, self-reported gender and OGTT-derived glucose and C-peptide measures. We trained and validated the models using halves of a dataset comprising autoantibody-positive first-degree relatives of individuals with type 1 diabetes (n=3967, 49

This study compares novel type 1 diabetes-related autoantibody assays developed to improve upon the standard radiobinding assay (RBA). Samples from 1505 individuals, followed for 5 years or to clinical type 1 diabetes, originally tested by RBA were aliquoted and sent blindly to 5 laboratories (BDC, IDR, DRI, MSD, Enable) to be tested by electrochemiluminescence (ECL) assays, Luciferase Immuno Precipitation System (LIPS) assays, multiplex antibody detection by agglutination-PCR (ADAP) assays, and N-terminally truncated GAD65 or IA2β autoantibody RBAs (tGADA/IA2βA). Findings: The fraction of samples that were concordant for negative/positive interpretations across all assays were 79.7% (GADA), 65.2% (IA-2A), 36.2% (IAA), and 67.5% (ZnT8A). The assays with the highest Youden index for predicting the previous RBA results differed by autoantibody: 0.65 LIPS(IDR) for IAA, 0.91 ECL(BDC) for ZnT8A, 0.82 tGADA RBA(IDR) for GADA, 0.91 ECL(MSD and BDC) for IA-2A. The Youden index for predicting 5-year type 1 diabetes varied significantly across assays and was highest for LIPS(DRI) for all autoantibody combinations, with little variation in the respective maximum Youden index. The discordance between assays makes it problematic to interpret positivity when comparing results from different assays. Longitudinal autoantibody assessments should be tested with the same assay.

Rationale: Pulmonary alveolar proteinosis (PAP) is a rare syndrome of surfactant accumulation that can result in respiratory failure. Autoimmune PAP (aPAP) accounts for ∼90% of all patients and is caused by granulocyte/macrophage-colony stimulating factor (GM-CSF) autoantibodies (GMAbs). Approximately 10% of cases are caused by other genetic and acquired conditions. Serum GMAb testing is 100% sensitive and specific for the diagnosis of aPAP. No FDA-approved therapy currently exists; patients are currently treated by whole lung lavage (WLL) and one of several off-label pharmacotherapies. Methods: The US National PAP Registry was initiated in 2015 to collect patient-reported questionnaire-based information regarding the presentation, diagnosis, and therapy of PAP-causing diseases. Information was also obtained by reviewing patients’ medical records. This report is focused on patients with aPAP. Data are mean ± SEM or percentages as appropriate. Results: Registry patients with aPAP (n=111) presented clinically at 38.6±1.5 years of age. The presenting symptoms included dyspnea (98%), fatigue (82%), cough (69%), sputum expectoration (61%), chest tightness (50%), and/or chest pain (45%). Less than half of the aPAP patients had a history of smoking. Diagnosis of aPAP occurred 1.0±0.2 years after the onset of symptoms and often followed a diagnosis of pneumonia (43%) or asthma (14%). Of those diagnosed with pneumonia, 93.5% received 2.8±0.7 courses of antibiotics before a diagnosis of aPAP was pursued. Medical records indicated 63 of 89 (71%) aPAP patients had a lung biopsy as part of their diagnostic evaluation; 30% had a surgical biopsy, 34% had a transbronchial biopsy, and 6.7% had both. None of the biopsies were diagnostic for aPAP and 9.5% failed to identify PAP. In all patients, the diagnosis of aPAP was made by serum GMAb testing, for which the median (and interquartile range) was 96.2 (57.6-182.5) mcg/ml. Most (86%) patients received therapy for PAP including WLL (79%), off-label GM-CSF (38%) (45% inhaled, 39% subcutaneous, 16% via both routes), corticosteroids (25%), or rituximab (8%). WLL was administered once in 25.5%, 2-5 times in 49%, and >5 times in 25.5% of patients; with 73% reporting a good response. Conclusions: Among US National PAP Registry participants, aPAP was the most common PAP-causing disease, dyspnea was the most common symptom, serum GMAb level was elevated and diagnostic in all aPAP patients while lung biopsies were unable to diagnose aPAP in any patient and failed to identify PAP in some. Studies evaluating the effectiveness of recombinant inhaled GM-CSF therapy are needed and ongoing.

Objective Childhood obesity may impact the risk of islet autoimmunity (IA). The trajectory of BMI through childhood resembles the early peak incidence of first-appearing autoantibodies against insulin (IAA-first) but not GAD65 (GADA-first). We studied if a child’s BMI can impact the age-related risk of first appearing IA phenotypes. Research Design and Methods We identified 7,724 children at risk of IA with at least three BMI measurements in the Environmental Determinants of Diabetes in the Young (TEDDY) study. We modeled the risk of IAA-first, GADA-first, and IA overall, on a child’s BMI z-score and change in BMI during infancy (2 weeks to 1.5 years, n=7,724), early childhood (age 1.5-8.5 years, n=6,396), and puberty (age 8.5-15 years, n=4,732) using joint modeling of longitudinal BMI and time-to-event IA. Results An infant’s BMI z-score was not associated with IA risk before 18 months of age (n=185, HR 1.03, 95% CI [0.88, 1.19]). In contrast, a child’s BMI correlated with an increased risk of IA from 1.5 to 8.5 years (n=470, HR 1.20, 95% CI [1.04, 1.32]) and from 8.5 to 15 years (n =209, HR 1.27, 95% CI [1.09,1.49]). No interactions with first appearing IA phenotypes were observed. However, high BMI z-score (SD > 0.5) from age 9 months increased the risk of IA in early childhood specifically for children with HLA-DR4/4,8 and not with HLA-DR3/3,4 (HLA*BMI interaction, p<0.005). Conclusions The contribution of BMI to risk of IA during early childhood is dependent on HLA-DR-DQ genotype more so than first appearing IA phenotype.

Established by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) in 2001, Type 1 Diabetes TrialNet (TrialNet) is an international consortium of clinical research centers that studies the development of type 1 diabetes and performs clinical studies aimed at delaying or preventing the disease. In recognition of NIDDK's 75th anniversary, this review will summarize the major findings, accomplishments, and future opportunities of its long-running program TrialNet. More than 20 intervention, observational, and mechanism-directed clinical studies have been conducted in collaboration with thousands of people living with type 1 diabetes and their families. New and repurposed immunotherapies have been successful in stages 2 and 3 of type 1 diabetes, contributing to the U.S. Food and Drug Administration approval of the first disease-modifying therapy to delay the onset of type 1 diabetes. Mechanistic findings continue to drive ongoing and future trial designs including novel combination therapies. TrialNet has several ongoing trials, with several for early stages of type 1 diabetes in development. There are new initiatives within TrialNet for community engagement, increasing clinical trial representation, personalizing treatments, and training the next generation of translational investigators.

Objective: To design a dynamic prediction model for estimating the time of progression from a single glutamic acid decarboxylase autoantibody (GADA) to multiple islet autoantibodies and type 1 diabetes in children, exploring different longitudinally measured risk variables. Research Design and Methods: GADA-positive children (n = 379) participating in The Environmental Determinants of Diabetes in the Young (TEDDY) study were followed for the appearance of additional autoantibodies against either insulin autoantibody (IAA), insulinoma-like 2 autoantibody (IA-2A), or zinc transporter 8 antibody (ZnT8A) and type 1 diabetes. A dynamic prediction model was designed, including trajectories of longitudinal risk variables, autoantibody titers, and metabolic variables (C-peptide, glucose, and HbA1c) together with time-invariant variables (gender, age at GADA positivity, and high-risk HLA genotypes). Results: Transition risk from GADA to multiple autoantibodies was increased by lower age (p < 0.001) and by increased GADA titers during follow-up (p < 0.001), and was less likely in children with HLA DQ2/X but not DQ2/8 (p=0.004). The transition risk from multiple autoantibodies without IA-2A to IA-2A positivity was associated with increased levels of 2 h glucose following oral glucose tolerance test (OGTT) (p < 0.001) and increased ZnT8A titers (p < 0.001). Increasing HbA1c (p < 0.001) and GADA titers (p < 0.001) were associated with an increased risk of transition from GADA only to type 1 diabetes; while increasing HbA1c (p < 0.001) was associated with the transition from multiple autoantibodies to type 1 diabetes. Risk of transition from multiple autoantibodies, including IA-2A to type 1 diabetes was also associated with 2 h glucose level (p < 0.001). Conclusion: The dynamic prediction model presented an individual time-specific risk of transition from a single GADA to multiple autoantibodies and type 1 diabetes.

Context Autoantibodies to thyroid peroxidase (TPOAb) and thyroglobulin (TgAb) define preclinical autoimmune thyroid disease (AITD), which can progress to either clinical hypothyroidism or hyperthyroidism.Objective We determined the age at seroconversion in children genetically at risk for type 1 diabetes.Methods TPOAb and TgAb seropositivity were determined in 5066 healthy children with human leukocyte antigen (HLA) DR3- or DR4-containing haplogenotypes from The Environmental Determinants of Diabetes in the Young (TEDDY) study. Children seropositive on the cross-sectional initial screen at age 8 to 13 years had longitudinally collected samples (from age 3.5 months) screened retrospectively and prospectively for thyroid autoantibodies to identify age at seroconversion. The first-appearing autoantibody was related to sex, HLA genotype, family history of AITD, and subsequent thyroid dysfunction and disease.Results The youngest appearance of TPOAb and TgAb was age 10 and 15 months, respectively. Girls had higher incidence rates of both autoantibodies. Family history of AITD was associated with a higher risk of TPOAb hazard ratio (HR) 1.90; 95% CI, 1.17-3.08; and TgAb HR 2.55; 95% CI, 1.91-3.41. The risk of progressing to hypothyroidism or hyperthyroidism was not different between TgAb and TPOAb, but children with both autoantibodies appearing at the same visit had a higher risk compared to TPOAb appearing first (HR 6.34; 95% CI, 2.72-14.76).Conclusion Thyroid autoantibodies may appear during the first years of life, especially in girls, and in children with a family history of AITD. Simultaneous appearance of both autoantibodies increases the risk for hypothyroidism or hyperthyroidism.

Type 1 diabetes (T1D) is a chronic autoimmune disease with a metabolic outcome. Studies over the past decades, have identified the contributions of genetics, environmental factors, and disorders of innate and adaptive immunity that collectively cause β-cell killing. The risk for T1D can be genetically identified but genotypes alone do not identify factors that lead to disease progression. The incidence of T1D has been increasing in the past few decades, which may be due to reduced exposure to infections and other environmental factors that can reduce autoimmunity (hygiene hypothesis). Once initiated, the disease pathogenesis progresses through stages that have been defined on the bases of immunologic (i.e., autoantibodies) and metabolic markers (glucose tolerance). The stages only loosely capture the risk for the time to diagnosis of disease, do not directly reflect disease activity, and there may be variance in the rate of progression within stages. In a general way, the stages can be used to identify patients at risk in whom interventions may be considered to modulate progression. This was achieved with the approval of teplizumab, a humanized anti-CD3 monoclonal antibody, for delaying the diagnosis of T1D.

OBJECTIVE:To compare the efficacy of abatacept to placebo for the treatment of relapsing, nonsevere granulomatosis with polyangiitis (GPA). METHODS:In this multicenter trial, eligible patients with relapsing, nonsevere GPA were randomized to receive abatacept 125 mg subcutaneously once a week or placebo, both together with prednisone 30 mg/day (or equivalent), tapered and discontinued at week 12. Patients already taking methotrexate, azathioprine, mycophenolate, or leflunomide continued this medication at a stable dose. Patients achieving remission remained on their randomized assignment until relapse, early termination, or the common close date 12 months after enrollment of the last patient. Those who had a nonsevere relapse, had nonsevere worsening, or were not in remission by month 6 had the option to receive open-label abatacept. The primary end point was the rate of treatment failure, defined as relapse, disease worsening, or failure to achieve a Birmingham Vasculitis Activity Score for Wegener's Granulomatosis (BVAS/WG) of 0 or 1 by six months. RESULTS:Sixty-five patients were randomized; 34 received abatacept and 31 received placebo. No statistical difference in the treatment failure rate was found between those who received abatacept and those who received placebo (P = 0.853). Treatment with abatacept did not demonstrate any statistical difference from placebo in key secondary end points, including time to full remission (BVAS/WG = 0), duration of glucocorticoid-free remission, relapse severity, prevention of damage, and patient-reported quality-of-life outcomes. There was no difference in the frequency or severity of adverse events between treatment arms, including infection. CONCLUSION:In patients with relapsing, nonsevere GPA, abatacept did not reduce the risk of relapse, severe worsening, or failure to achieve remission.

Delivery by Caesarean section continues to rise globally and has been associated with the risk of developing type 1 diabetes and the rate of progression from pre-symptomatic stage 1 or 2 type 1 diabetes to symptomatic stage 3 disease. The aim of this study was to examine the association between Caesarean delivery and progression to stage 3 type 1 diabetes in children with pre-symptomatic early-stage type 1 diabetes. Caesarean section was examined in 8135 children from the TEDDY study who had an increased genetic risk for type 1 diabetes and were followed from birth for the development of islet autoantibodies and type 1 diabetes. The likelihood of delivery by Caesarean section was higher in children born to mothers with type 1 diabetes (adjusted OR 4.61, 95 https://doi.org/10.58020/y3jk-x087 ) reported here will be made available for request at the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repository (NIDDK-CR) Resources for Research (R4R) ( https://repository.niddk.nih.gov/ ).

Introduction & Objective: TN10 Anti-CD3 Prevention (TN10) was a randomized phase 2 clinical trial that showed teplizumab delayed progression to type 1 diabetes (T1D) in high-risk participants. Both HLA and non-HLA variants could influence time to progression. Here, genome-wide analysis identified variants and pathways that influence time to progression in TN10 participants. Methods: In TN10, relatives with stage 2 T1D (i.e., multiple autoantibodies and dysglycemia) received either teplizumab (N = 44) or placebo (N = 32). Samples were genotyped with a genome-wide array followed by imputation. Cox proportional hazards regression models were used to determine the effect of teplizumab, SNPs, and their interaction on time to progression to stage 3 T1D. Thousands of Polygenic Scores (PGSs) from the PGS catalogue were inferred for each of the TN10 samples, and we identified PGS traits that shared common genetic modifiers with time to progression with teplizumab. Results: A genome-wide analysis identified three loci associated with time to progression (p < 5 x 10-6). Two loci contain genes implicated in the inflammatory response (NFKBIZ) and drug metabolism effect (FMO3). SNP-drug interaction analysis identified four known T1D regions that account for progression differences in teplizumab vs placebo: CCR9 (rs34549672), SH2B3 (rs3184504), UBASH3A (rs9984852), and INS (rs3842761). Within the teplizumab group, novel loci (p < 5 x 10-6) were associated with time to progression, including ZNF385D, CCDC38, SHH, ZNF366, ITPKB and RABGAP1L. Traits with similar genetic contribution to teplizumab time to progression were vitamin B12 (AUC = 0.76) and vitamin D (AUC = 0.72). Conclusions: In individuals with stage 2 T1D, variants in inflammatory, immune-relevant, and drug-responsive genes are associated with teplizumab time to progression. Similarity of the teplizumab-responsive polygenic score with other traits implicate novel pathways that could influence teplizumab treatment. Disclosure D.A. Michalek: None. S. Onengut-Gumuscu: None. W. Chen: None. T.M. Brusko: None. A. Steck: None. P. Gottlieb: Other Relationship; IM Therapeutics. Research Support; Imcyse. Advisory Panel; Imcyse. Consultant; Juvenile Diabetes Research Foundation (JDRF). Research Support; Hemsley Charitable Trust, Novartis AG, Provention Bio, Inc., Precigen, Inc. Advisory Panel; ViaCyte, Inc. Research Support; Nova Pharmaceuticals. R.A. Oram: Research Support; Randox R & D. Consultant; Provention Bio, Inc., Sanofi. J. Krischer: None. H.M. Parikh: None. M.J. Redondo: None. K.C. Herold: Consultant; Sanofi. S.S. Rich: None. Funding National Institutes of Health (1R01DK121843-01)

Traditional medical research infrastructures relying on the Centers of Excellence (CoE) model (an infrastructure or shared facility providing high standards of research excellence and resources to advance scientific knowledge) are often limited by geographic reach regarding patient accessibility, presenting challenges for study recruitment and accrual. Thus, the development of novel, patient-centered (PC) strategies (e.g., the use of online technologies) to support recruitment and streamline study procedures are necessary. This research focused on an implementation evaluation of a design innovation with implementation outcomes as communicated by study staff and patients for CoE and PC approaches for a randomized controlled trial (RCT) for patients with vasculitis. In-depth qualitative interviews were conducted with 32 individuals (17 study team members, 15 patients). Transcripts were coded using the Consolidated Framework for Implementation Research (CFIR). The following CFIR elements emerged: characteristics of the intervention, inner setting, characteristics of individuals, and process. From the staff perspective, the communication of the PC approach was a major challenge, but should have been used as an opportunity to identify one “point person” in charge of all communicative elements among the study team. Study staff from both arms were highly supportive of the PC approach and saw its promise, particularly regarding online consent procedures. Patients reported high self-efficacy in reference to the PC approach and utilization of online technologies. Local physicians were integral for making patients feel comfortable about participation in research studies. The complexity of replicating the interpersonal nature of the CoE model in the virtual setting is substantial, meaning the PC approach should be viewed as a hybrid strategy that integrates online and face-to-face practices. 1) Name: The Assessment of Prednisone In Remission Trial – Centers of Excellence Approach (TAPIR). Trial registration number: ClinicalTrials.gov NCT01940094 . Date of registration: September 10, 2013. 2) Name: The Assessment of Prednisone In Remission Trial – Patient Centric Approach (TAPIR). Trial registration number: Clinical Trials.gov NCT01933724 . Date of registration: September 2, 2013.