Identification of type 1 diabetes risk phenotypes using an outcome-guided clustering analysis.

AIMS/HYPOTHESIS: Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk. METHODS: We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123). The outcome of the analysis was the time to development of type 1 diabetes, and variables in the model included demographic characteristics, genetics, metabolic factors and islet autoantibodies. An independent dataset (the Diabetes Prevention Trial of Type 1 Diabetes Study) (n=706) was used for validation. RESULTS: The analysis revealed six clusters with varying type 1 diabetes risks, categorised into three groups based on the hierarchy of clusters. Group A comprised one cluster with high glucose levels (median for glucose mean AUC 9.48 mmol/l; IQR 9.16-10.02) and high risk (2-year diabetes-free survival probability 0.42; 95% CI 0.34, 0.51). Group B comprised one cluster with high IA-2A titres (median 287 DK units/ml; IQR 250-319) and elevated autoantibody titres (2-year diabetes-free survival probability 0.73; 95% CI 0.67, 0.80). Group C comprised four lower-risk clusters with lower autoantibody titres and glucose levels (with 2-year diabetes-free survival probability ranging from 0.84-0.99 in the four clusters). Within group C, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels and age. A decision rule for assigning individuals to clusters was developed. Use of the validation dataset confirmed that the clusters can identify individuals with similar characteristics. CONCLUSIONS/INTERPRETATION: Demographic, metabolic, immunological and genetic markers may be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.

The Pathogenesis of Type 1 Diabetes.

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 ß-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.

Evolving Concepts in Pathophysiology, Screening, and Prevention of Type 1 Diabetes: Report of Diabetes Mellitus Interagency Coordinating Committee Workshop.

The approval of teplizumab to delay the onset of type 1 diabetes is an important inflection point in the decades long pursuit to treat the cause of the disease rather than its symptoms. NIDDK convened a workshop of the Diabetes Mellitus Interagency Coordinating Committee titled "Evolving Concepts in Pathophysiology, Screening, and Prevention of Type 1 Diabetes" to review this accomplishment and identify future goals. Speakers representing Diabetes TrialNet (TN) and the Immune tolerance Network (ITN) emphasized that the ability to robustly identify individuals destined to develop type 1 diabetes was essential for clinical trials. The presenter from the FDA described that regulatory approval relied upon data from TN's single clinical trial testing teplizumab for delay of clinical diagnosis, along with confirmatory evidence from studies in patients after diagnosis. The workshop reviewed the etiology of type 1 diabetes as a disease involving multiple immune pathways, highlighting the current understanding of prognostic markers, and proposing potential strategies to improve the therapeutic response of disease modifying therapies based on their mechanism of action. While celebrating these achievements funded by the congressionally appropriated Special Diabetes Program, panelists from professional organizations, non-profit advocacy/funding groups, and industry also identified significant hurdles in translating this research into clinical care.

Early appearance of thyroid autoimmunity in children followed from birth for type 1 diabetes risk.

PURPOSE: Autoantibodies to thyroid peroxidase (TPOAb) and thyroglobulin (TgAb) define pre-clinical autoimmune thyroid disease (AITD) which can progress to either clinical hypo- or hyperthyroidism. 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 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 8-13 years of age had longitudinally collected samples (from 3.5 months of age) screened retrospectively and prospectively for thyroid autoantibodies to identify the age at seroconversion. 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 10 and 15 months of age, 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% confidence interval [CI] 1.17, 3.08; and TgAb HR 2.55, 95% CI 1.91, 3.41. The risk of progressing to hypo- 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). MAIN 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 hypo- or hyperthyroidism.

High Concentrations of Immunoglobulin G Against Cow Milk Proteins and Frequency of Cow Milk Consumption Are Associated With the Development of Islet Autoimmunity and Type 1 Diabetes-The Trial to Reduce Insulin-dependent Diabetes Mellitus (IDDM) in the Genetically at Risk (TRIGR) Study.

BACKGROUND: The Trial to Reduce IDDM in the Genetically at Risk (TRIGR) (NCT00179777) found no difference type 1 diabetes risk between hydrolyzed and regular infant formula. However, cow milk consumption during childhood is consistently linked to type 1 diabetes risk in prospective cohort studies. OBJECTIVES: Our primary aim was to study whether humoral immune responses to cow milk and cow milk consumption are associated with type 1 diabetes in TRIGR children. METHODS: TRIGR comprised 2159 children with genetic susceptibility to type 1 diabetes born between 2002 and 2007 in 15 countries. Children were randomly assigned into groups receiving extensively hydrolyzed casein or a regular cow milk formula and followed up until age 10 y. Type 1 diabetes-related autoantibodies and antibodies to cow milk proteins were analyzed. Infant formula intake was measured by structured dietary interviews and milk consumption with a food frequency questionnaire. Associations of milk antibodies and milk consumption with risk to develop type 1 diabetes were analyzed using Cox survival model. RESULTS: Cow milk antibody concentrations both in cord blood [hazards ratio (HR) for islet autoimmunity: 1.30; 95% CI: 1.05, 1.61; HR for type 1 diabetes: 1.32; 95% CI: 1.02, 1.71] and longitudinally from birth to 3 years (HR for islet autoimmunity: 1.39; 95% CI: 1.07, 1.81; HR for type 1 diabetes: 1.43; 95% CI: 1.04, 1.96) were associated with increased risk of developing type 1 diabetes. The amount of regular infant formula was associated with reduced islet autoimmunity risk in the regular infant formula group (HR: 0.92; 95% CI: 0.85, 0.99). Furthermore, frequent liquid milk consumption after infancy was associated with increased risk of islet autoimmunity or type 1 diabetes. CONCLUSIONS: Elevated cow milk antibody concentrations and high consumption of liquid milk after infancy are related to type 1 diabetes development in children with an increased genetic susceptibility to type 1 diabetes. Enhanced antibody concentrations to cow milk may provide a biomarker of immune system prone to develop islet autoimmunity. This trial was registered at clinicaltrials.gov as NCT00179777.

The Influence of Pubertal Development on Autoantibody Appearance and Progression to Type 1 Diabetes in the TEDDY Study.

Context: The 2 peaks of type 1 diabetes incidence occur during early childhood and puberty. Objective: We sought to better understand the relationship between puberty, islet autoimmunity, and type 1 diabetes. Methods: The relationships between puberty, islet autoimmunity, and progression to type 1 diabetes were investigated prospectively in children followed in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Onset of puberty was determined by subject self-assessment of Tanner stages. Associations between speed of pubertal progression, pubertal growth, weight gain, homeostasis model assessment of insulin resistance (HOMA-IR), islet autoimmunity, and progression to type 1 diabetes were assessed. The influence of individual factors was analyzed using Cox proportional hazard ratios. Results: Out of 5677 children who were still in the study at age 8 years, 95% reported at least 1 Tanner Stage score and were included in the study. Children at puberty (Tanner Stage ≥2) had a lower risk (HR 0.65, 95% CI 0.45-0.93; P = .019) for incident autoimmunity than prepubertal children (Tanner Stage 1). An increase of body mass index Z-score was associated with a higher risk (HR 2.88, 95% CI 1.61-5.15; P < .001) of incident insulin autoantibodies. In children with multiple autoantibodies, neither HOMA-IR nor rate of progression to Tanner Stage 4 were associated with progression to type 1 diabetes. Conclusion: Rapid weight gain during puberty is associated with development of islet autoimmunity. Puberty itself had no significant influence on the appearance of autoantibodies or type 1 diabetes. Further studies are needed to better understand the underlying mechanisms.

Genetic Associations with C-peptide Levels before Type 1 Diabetes Diagnosis in At-Risk Relatives.

OBJECTIVE: We sought to determine whether the type 1 diabetes genetic risk score-2 (T1D-GRS2) and single nucleotide polymorphisms (SNPs) are associated with C-peptide preservation before type 1 diabetes diagnosis. METHODS: We conducted a retrospective analysis of 713 autoantibody-positive participants who developed type 1 diabetes in the TrialNet Pathway to Prevention Study who had T1DExomeChip data. We evaluated the relationships of 16 known SNPs and T1D-GRS2 with area under the curve (AUC) C-peptide levels during oral glucose tolerance tests conducted in the 9 months before diagnosis. RESULTS: Higher T1D-GRS2 was associated with lower C-peptide AUC in the 9 months before diagnosis in univariate (ß=-0.06, P<0.0001) and multivariate (ß=-0.03, P=0.005) analyses. Participants with the JAZF1 rs864745 T allele had lower C-peptide AUC in both univariate (ß=-0.11, P=0.002) and multivariate (ß=-0.06, P=0.018) analyses. CONCLUSIONS: The type 2 diabetes-associated JAZF1 rs864745 T allele and higher T1D-GRS2 are associated with lower C-peptide AUC prior to diagnosis of type 1 diabetes, with implications for the design of prevention trials.

Caesarean section and risk of type 1 diabetes.

AIMS/HYPOTHESIS: 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. METHODS: 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. RESULTS: The likelihood of delivery by Caesarean section was higher in children born to mothers with type 1 diabetes (adjusted OR 4.61, 95% CI 3.60, 5.90, p<0.0001), in non-singleton births (adjusted OR 4.35, 95% CI 3.21, 5.88, p<0.0001), in premature births (adjusted OR 1.91, 95% CI 1.53, 2.39, p<0.0001), in children born in the USA (adjusted OR 2.71, 95% CI 2.43, 3.02, p<0.0001) and in children born to older mothers (age group >28-33 years: adjusted OR 1.19, 95% CI 1.04, 1.35, p=0.01; age group >33 years: adjusted OR 1.80, 95% CI 1.58, 2.06, p<0.0001). Caesarean section was not associated with an increased risk of developing pre-symptomatic early-stage type 1 diabetes (risk by age 10 years 5.7% [95% CI 4.6%, 6.7%] for Caesarean delivery vs 6.6% [95% CI 6.0%, 7.3%] for vaginal delivery, p=0.07). Delivery by Caesarean section was associated with a modestly increased rate of progression to stage 3 type 1 diabetes in children who had developed multiple islet autoantibody-positive pre-symptomatic early-stage type 1 diabetes (adjusted HR 1.36, 95% CI 1.03, 1.79, p=0.02). No interaction was observed between Caesarean section and non-HLA SNPs conferring susceptibility for type 1 diabetes. CONCLUSIONS/INTERPRETATION: Caesarean section increased the rate of progression to stage 3 type 1 diabetes in children with pre-symptomatic early-stage type 1 diabetes. DATA AVAILABILITY: Data from the TEDDY study ( 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/ ).

A discrete approximation method for modeling interval-censored multistate data.

Many longitudinal studies are designed to monitor participants for major events related to the progression of diseases. Data arising from such longitudinal studies are usually subject to interval censoring since the events are only known to occur between two monitoring visits. In this work, we propose a new method to handle interval-censored multistate data within a proportional hazards model framework where the hazard rate of events is modeled by a nonparametric function of time and the covariates affect the hazard rate proportionally. The main idea of this method is to simplify the likelihood functions of a discrete-time multistate model through an approximation and the application of data augmentation techniques, where the assumed presence of censored information facilitates a simpler parameterization. Then the expectation-maximization algorithm is used to estimate the parameters in the model. The performance of the proposed method is evaluated by numerical studies. Finally, the method is employed to analyze a dataset on tracking the advancement of coronary allograft vasculopathy following heart transplantation.

imply: improving cell-type deconvolution accuracy using personalized reference profiles.

Using computational tools, bulk transcriptomics can be deconvoluted to estimate the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, ignoring person-to-person heterogeneity. Here, we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. Simulation studies demonstrate reduced bias compared with existing methods. Real data analyses on longitudinal consortia show disparities in cell type proportions are associated with several disease phenotypes in Type 1 diabetes and Parkinson's disease. imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/ .

A toxicology study of Csf2ra complementation and pulmonary macrophage transplantation therapy of hereditary PAP in mice.

Pulmonary macrophage transplantation (PMT) is a gene and cell transplantation approach in development as therapy for hereditary pulmonary alveolar proteinosis (hPAP), a surfactant accumulation disorder caused by mutations in CSF2RA/B (and murine homologs). We conducted a toxicology study of PMT of Csf2ra gene-corrected macrophages (mGM-Rα+Mϕs) or saline-control intervention in Csf2raKO or wild-type (WT) mice including single ascending dose and repeat ascending dose studies evaluating safety, tolerability, pharmacokinetics, and pharmacodynamics. Lentiviral-mediated Csf2ra cDNA transfer restored GM-CSF signaling in mGM-Rα+Mϕs. Following PMT, mGM-Rα+Mϕs engrafted, remained within the lungs, and did not undergo uncontrolled proliferation or result in bronchospasm, pulmonary function abnormalities, pulmonary or systemic inflammation, anti-transgene product antibodies, or pulmonary fibrosis. Aggressive male fighting caused a similarly low rate of serious adverse events in saline- and PMT-treated mice. Transient, minor pulmonary neutrophilia and exacerbation of pre-existing hPAP-related lymphocytosis were observed 14 days after PMT of the safety margin dose but not the target dose (5,000,000 or 500,000 mGM-Rα+Mϕs, respectively) and only in Csf2raKO mice but not in WT mice. PMT reduced lung disease severity in Csf2raKO mice. Results indicate PMT of mGM-Rα+Mϕs was safe, well tolerated, and therapeutically efficacious in Csf2raKO mice, and established a no adverse effect level and 10-fold safety margin.

Intake of B vitamins and the risk of developing islet autoimmunity and type 1 diabetes in the TEDDY study.

PURPOSE: The aim was to study the association between dietary intake of B vitamins in childhood and the risk of islet autoimmunity (IA) and progression to type 1 diabetes (T1D) by the age of 10 years. METHODS: We followed 8500 T1D-susceptible children born in the U.S., Finland, Sweden, and Germany in 2004 -2010 from the Environmental Determinants of Diabetes in the Young (TEDDY) study, which is a prospective observational birth cohort. Dietary intake of seven B vitamins was calculated from foods and dietary supplements based on 24-h recall at 3 months and 3-day food records collected regularly from 6 months to 10 years of age. Cox proportional hazard models were adjusted for energy, HLA-genotype, first-degree relative with T1D, sex, and country. RESULTS: A total of 778 (9.2) children developed at least one autoantibody (any IA), and 335 (3.9%) developed multiple autoantibodies. 280 (3.3%) children had IAA and 319 (3.8%) GADA as the first autoantibody. 344 (44%) children with IA progressed to T1D. We observed that higher intake of niacin was associated with a decreased risk of developing multiple autoantibodies (HR 0.95; 95% CI 0.92, 0.98) per 1 mg/1000 kcal in niacin intake. Higher intake of pyridoxine (HR 0.66; 95% CI 0.46, 0.96) and vitamin B12 (HR 0.87; 95% CI 0.77, 0.97) was associated with a decreased risk of IAA-first autoimmunity. Higher intake of riboflavin (HR 1.38; 95% CI 1.05, 1.80) was associated with an increased risk of GADA-first autoimmunity. There were no associations between any of the B vitamins and the outcomes "any IA" and progression from IA to T1D.  CONCLUSION: In this multinational, prospective birth cohort of children with genetic susceptibility to T1D, we observed some direct and inverse associations between different B vitamins and risk of IA.

Detecting potential outliers in longitudinal data with time-dependent covariates.

BACKGROUND: Outliers can influence regression model parameters and change the direction of the estimated effect, over-estimating or under-estimating the strength of the association between a response variable and an exposure of interest. Identifying visit-level outliers from longitudinal data with continuous time-dependent covariates is important when the distribution of such variable is highly skewed. OBJECTIVES: The primary objective was to identify potential outliers at follow-up visits using interquartile range (IQR) statistic and assess their influence on estimated Cox regression parameters. METHODS: Study was motivated by a large TEDDY dietary longitudinal and time-to-event data with a continuous time-varying vitamin B12 intake as the exposure of interest and development of Islet Autoimmunity (IA) as the response variable. An IQR algorithm was applied to the TEDDY dataset to detect potential outliers at each visit. To assess the impact of detected outliers, data were analyzed using the extended time-dependent Cox model with robust sandwich estimator. Partial residual diagnostic plots were examined for highly influential outliers. RESULTS: Extreme vitamin B12 observations that were cases of IA had a stronger influence on the Cox regression model than non-cases. Identified outliers changed the direction of hazard ratios, standard errors, or the strength of association with the risk of developing IA. CONCLUSION: At the exploratory data analysis stage, the IQR algorithm can be used as a data quality control tool to identify potential outliers at the visit level, which can be further investigated.

Associations of dietary patterns between age 9 and 24 months with risk of celiac disease autoimmunity and celiac disease among children at increased risk.

BACKGROUND: Higher gluten intake in childhood is associated with increased incidence of celiac disease autoimmunity (CDA) and celiac disease. It remains to be studied whether different dietary patterns independent of gluten intake contribute to the incidence. OBJECTIVES: This study aimed to explore associations of dietary patterns by age 2 y with risk of CDA and celiac disease in genetically susceptible children. METHODS: Data was used from 6726 participants at genetic risk of type 1 diabetes and celiac disease enrolled in the observational cohort, The Environmental Determinants of Diabetes in the Young (TEDDY) study. Children were annually screened for tissue transglutaminase autoantibodies (tTGAs) from age 2 y. Principal component analysis extracted dietary patterns, based on intake of 27 food groups assessed by 3-d food records at age 9 to 24 mo. The primary outcome was CDA (i.e., persistently tTGA-positive in at least 2 consecutive samples), and the secondary outcome was celiac disease. During follow-up to mean age 11.0 (standard deviation 3.6) y, 1296 (19.3%) children developed CDA, and 529 (7.9%) were diagnosed with celiac disease. Associations of adherence to dietary patterns (per 5-unit increase) with the study outcomes were estimated by Cox regression models adjusted for risk factors including gluten intake. RESULTS: At age 9 mo, a dietary pattern higher in the food groups vegetable fats and milk was associated with reduced risk of CDA (hazard ratio [HR]: 0.88; 95% confidence interval [CI]: 0.79, 0.98; P = 0.02). At 24 mo, a dietary pattern higher in the food groups wheat, vegetable fats, and juices, and lower in milk, meat, and oats at age 24 mo was associated with increased risk of CDA (HR: 1.18; 95% CI: 1.05, 1.33; P < 0.001) and celiac disease (HR: 1.24; 95% CI: 1.03, 1.50; P = 0.03). CONCLUSIONS: Dietary patterns in early childhood are associated with risk of CDA and celiac disease in genetically predisposed children, independent of gluten intake.

Distinct transcriptomic profiles in children prior to the appearance of type 1 diabetes-linked islet autoantibodies and following enterovirus infection.

Although the genetic basis and pathogenesis of type 1 diabetes have been studied extensively, how host responses to environmental factors might contribute to autoantibody development remains largely unknown. Here, we use longitudinal blood transcriptome sequencing data to characterize host responses in children within 12 months prior to the appearance of type 1 diabetes-linked islet autoantibodies, as well as matched control children. We report that children who present with insulin-specific autoantibodies first have distinct transcriptional profiles from those who develop GADA autoantibodies first. In particular, gene dosage-driven expression of GSTM1 is associated with GADA autoantibody positivity. Moreover, compared with controls, we observe increased monocyte and decreased B cell proportions 9-12 months prior to autoantibody positivity, especially in children who developed antibodies against insulin first. Lastly, we show that control children present transcriptional signatures consistent with robust immune responses to enterovirus infection, whereas children who later developed islet autoimmunity do not. These findings highlight distinct immune-related transcriptomic differences between case and control children prior to case progression to islet autoimmunity and uncover deficient antiviral response in children who later develop islet autoimmunity.

imply: improving cell-type deconvolution accuracy using personalized reference profiles.

Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, which ignores person-to-person heterogeneity. Here we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. imply can borrow information across repeatedly measured samples for each subject, and obtain precise cell type proportion estimations. Simulation studies demonstrate reduced bias in cell type abundance estimation compared with existing methods. Real data analyses on large longitudinal consortia show more realistic deconvolution results that align with biological facts. Our results suggest that disparities in cell type proportions are associated with several disease phenotypes in type 1 diabetes and Parkinson's disease. Our proposed tool imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/.

Type 1 Diabetes Risk Phenotypes Using Cluster Analysis.

Background: Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal clinically meaningful clusters in the at-risk population and allow for non-linear relationships between predictors are lacking. We aimed to identify and characterize clusters of islet autoantibody-positive individuals that share similar characteristics and type 1 diabetes risk. Methods: We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention (PTP) study data (n=1127). The outcome of the analysis was time to type 1 diabetes and variables in the model included demographics, genetics, metabolic factors and islet autoantibodies. An independent dataset (Diabetes Prevention Trial of Type 1 Diabetes, DPT-1 study) (n=704) was used for validation. Findings: The analysis revealed 8 clusters with varying type 1 diabetes risks, categorized into three groups. Group A had three clusters with high glucose levels and high risk. Group B included four clusters with elevated autoantibody titers. Group C had three lower-risk clusters with lower autoantibody titers and glucose levels. Within the groups, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels, age, and genetic risk. A decision rule for assigning individuals to clusters was developed. The validation dataset confirms that the clusters can identify individuals with similar characteristics. Interpretation: Demographic, metabolic, immunological, and genetic markers can be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.

Hydroxychloroquine in Stage 1 Type 1 Diabetes.

OBJECTIVE: Innate immune responses may be involved in the earliest phases of type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: To test whether blocking innate immaune cells modulated progression of the disease, we randomly assigned 273 individuals with stage 1 T1D to treatment with hydroxychloroquine (n = 183; 5 mg/kg per day to a maximum of 400 mg) or placebo (n = 90) and assessed whether hydroxychloroquine treatment delayed or prevented progression to stage 2 T1D (i.e., two or more islet autoantibodies with abnormal glucose tolerance). RESULTS: After a median follow-up of 23.3 months, the trial was stopped prematurely by the data safety monitoring board because of futility. There were no safety concerns in the hydroxychloroquine arm, including in annual ophthalmologic examinations. Preplanned secondary analyses showed a transient decrease in the glucose average area under the curve to oral glucose in the hydroxychloroquine-treated arm at month 6 and reduced titers of anti-GAD and anti-insulin autoantibodies and acquisition of positive autoantibodies in the hydroxychloroquine arm (P = 0.032). CONCLUSIONS: We conclude that hydroxychloroquine does not delay progression to stage 2 T1D in individuals with stage 1 disease. Drug treatment reduces the acquisition of additional autoantibodies and the titers of autoantibodies to GAD and insulin.

HLA Genotype and Probiotics Modify the Association Between Timing of Solid Food Introduction and Islet Autoimmunity in the TEDDY Study.

OBJECTIVE: To study the interaction among HLA genotype, early probiotic exposure, and timing of complementary foods in relation to risk of islet autoimmunity (IA). RESEARCH DESIGN AND METHODS: The Environmental Determinants of Diabetes in the Young (TEDDY) study prospectively follows 8,676 children with increased genetic risk of type 1 diabetes. We used a Cox proportional hazards regression model adjusting for potential confounders to study early feeding and the risk of IA in a sample of 7,770 children. RESULTS: Any solid food introduced early (<6 months) was associated with increased risk of IA if the child had the HLA DR3/4 genotype and no probiotic exposure during the 1st year of life. Rice introduced at 4-5.9 months compared with later in the U.S. was associated with an increased risk of IA. CONCLUSIONS: Timing of solid food introduction, including rice, may be associated with IA in children with the HLA DR3/4 genotype not exposed to probiotics. The microbiome composition under these exposure combinations requires further study.

Dietary Intake and Body Mass Index Influence the Risk of Islet Autoimmunity in Genetically At-Risk Children: A Mediation Analysis Using the TEDDY Cohort.

Background/Objective: Growth and obesity have been associated with increased risk of islet autoimmunity (IA) and progression to type 1 diabetes. We aimed to estimate the effect of energy-yielding macronutrient intake on the development of IA through BMI. Research Design and Methods: Genetically at-risk children (n = 5,084) in Finland, Germany, Sweden, and the USA, who were autoantibody negative at 2 years of age, were followed to the age of 8 years, with anthropometric measurements and 3-day food records collected biannually. Of these, 495 (9.7%) children developed IA. Mediation analysis for time-varying covariates (BMI z-score) and exposure (energy intake) was conducted. Cox proportional hazard method was used in sensitivity analysis. Results: We found an indirect effect of total energy intake (estimates: indirect effect 0.13 [0.05, 0.21]) and energy from protein (estimates: indirect effect 0.06 [0.02, 0.11]), fat (estimates: indirect effect 0.03 [0.01, 0.05]), and carbohydrates (estimates: indirect effect 0.02 [0.00, 0.04]) (kcal/day) on the development of IA. A direct effect was found for protein, expressed both as kcal/day (estimates: direct effect 1.09 [0.35, 1.56]) and energy percentage (estimates: direct effect 72.8 [3.0, 98.0]) and the development of GAD autoantibodies (GADA). In the sensitivity analysis, energy from protein (kcal/day) was associated with increased risk for GADA, hazard ratio 1.24 (95% CI: 1.09, 1.53), p = 0.042. Conclusions: This study confirms that higher total energy intake is associated with higher BMI, which leads to higher risk of the development of IA. A diet with larger proportion of energy from protein has a direct effect on the development of GADA.