The Influence of Type 1 Diabetes Genetic Susceptibility Regions, Age, Sex, and Family History on the Progression From Multiple Autoantibodies to Type 1 Diabetes: A TEDDY Study Report

Diabetes mellitus-Progress and opportunities in the evolving epidemic

Diabetes, a complex multisystem metabolic disorder characterized by hyperglycemia, leads to complications that reduce quality of life and increase mortality. Diabetes pathophysiology includes dysfunction of beta cells, adipose tissue, skeletal muscle, and liver. Type 1 diabetes (T1D) results from immune-mediated beta cell destruction. The more prevalent type 2 diabetes (T2D) is a heterogeneous disorder characterized by varying degrees of beta cell dysfunction in concert with insulin resistance. The strong association between obesity and T2D involves pathways regulated by the central nervous system governing food intake and energy expenditure, integrating inputs from peripheral organs and the environment. The risk of developing diabetes or its complications represents interactions between genetic susceptibility and environmental factors, including the availability of nutritious food and other social determinants of health. This perspective reviews recent advances in understanding the pathophysiology and treatment of diabetes and its complications, which could alter the course of this prevalent disorder.

Prediction of progression to type 1 diabetes with dynamic biomarkers and risk scores

Identifying biomarkers of functional β-cell loss is an important step in the risk stratification of type 1 diabetes. Genetic risk scores (GRS), generated by profiling an array of single nucleotide polymorphisms, are a widely used type 1 diabetes risk-prediction tool. Type 1 diabetes screening studies have relied on a combination of biochemical (autoantibody) and GRS screening methodologies for identifying individuals at high-risk of type 1 diabetes. A limitation of these screening tools is that the presence of autoantibodies marks the initiation of β-cell loss, and is therefore not the best biomarker of progression to early-stage type 1 diabetes. GRS, on the other hand, represents a static biomarker offering a single risk score over an individual's lifetime. In this Personal View, we explore the challenges and opportunities of static and dynamic biomarkers in the prediction of progression to type 1 diabetes. We discuss future directions wherein newer dynamic risk scores could be used to predict type 1 diabetes risk, assess the efficacy of new and emerging drugs to retard, or prevent type 1 diabetes, and possibly replace or further enhance the predictive ability offered by static biomarkers, such as GRS.

A set of circulating microRNAs belonging to the 14q32 chromosome locus identifies two subgroups of individuals with recent-onset type 1 diabetes

Circulating microRNAs (miRNAs) are linked to the onset and progression of type 1 diabetes mellitus (T1DM), thus representing potential disease biomarkers. In this study, we employed a multiplatform sequencing approach to analyze circulating miRNAs in an extended cohort of prospectively evaluated recent-onset T1DM individuals from the INNODIA consortium. Our findings reveal that a set of miRNAs located within T1DM susceptibility chromosomal locus 14q32 distinguishes two subgroups of individuals. To validate our results, we conducted additional analyses on a second cohort of T1DM individuals, confirming the identification of these subgroups, which we have named cluster A and cluster B. Remarkably, cluster B T1DM individuals, who exhibit increased expression of a set of 14q32 miRNAs, show better glycemic control and display a different blood immunomics profile. Our findings suggest that this set of circulating miRNAs can identify two different T1DM subgroups with distinct blood immunomics at baseline and clinical outcomes during follow-up.

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 (β=-0.06, P<0.0001) and multivariate (β=-0.03, P=0.005) analyses. Participants with the JAZF1 rs864745 T allele had lower C-peptide AUC in both univariate (β=-0.11, P=0.002) and multivariate (β=-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.

Lipidomic studies revealing serological markers associated with the occurrence of retinopathy in type 2 diabetes

PURPOSE

The duration of type 2 diabetes mellitus (T2DM) and blood glucose levels have a significant impact on the development of T2DM complications. However, currently known risk factors are not good predictors of the onset or progression of diabetic retinopathy (DR). Therefore, we aimed to investigate the differences in the serum lipid composition in patients with T2DM, without and with DR, and search for potential serological indicators associated with the development of DR.

METHODS

A total of 622 patients with T2DM hospitalized in the Department of Endocrinology of the First Affiliated Hospital of Xi'an JiaoTong University were selected as the discovery set. One-to-one case-control matching was performed according to the traditional risk factors for DR (i.e., age, duration of diabetes, HbA1c level, and hypertension). All cases with comorbid chronic kidney disease were excluded to eliminate confounding factors. A total of 42 pairs were successfully matched. T2DM patients with DR (DR group) were the case group, and T2DM patients without DR (NDR group) served as control subjects. Ultra-performance liquid chromatography-mass spectrometry (LC-MS/MS) was used for untargeted lipidomics analysis on serum, and a partial least squares discriminant analysis (PLS-DA) model was established to screen differential lipid molecules based on variable importance in the projection (VIP) > 1. An additional 531 T2DM patients were selected as the validation set. Next, 1:1 propensity score matching (PSM) was performed for the traditional risk factors for DR, and a combined 95 pairings in the NDR and DR groups were successfully matched. The screened differential lipid molecules were validated by multiple reaction monitoring (MRM) quantification based on mass spectrometry.

RESULTS

The discovery set showed no differences in traditional risk factors associated with the development of DR (i.e., age, disease duration, HbA1c, blood pressure, and glomerular filtration rate). In the DR group compared with the NDR group, the levels of three ceramides (Cer) and seven sphingomyelins (SM) were significantly lower, and one phosphatidylcholine (PC), two lysophosphatidylcholines (LPC), and two SMs were significantly higher. Furthermore, evaluation of these 15 differential lipid molecules in the validation sample set showed that three Cer and SM(d18:1/24:1) molecules were substantially lower in the DR group. After excluding other confounding factors (e.g., sex, BMI, lipid-lowering drug therapy, and lipid levels), multifactorial logistic regression analysis revealed that a lower abundance of two ceramides, i.e., Cer(d18:0/22:0) and Cer(d18:0/24:0), was an independent risk factor for the occurrence of DR in T2DM patients.

CONCLUSION

Disturbances in lipid metabolism are closely associated with the occurrence of DR in patients with T2DM, especially in ceramides. Our study revealed for the first time that Cer(d18:0/22:0) and Cer(d18:0/24:0) might be potential serological markers for the diagnosis of DR occurrence in T2DM patients, providing new ideas for the early diagnosis of DR.

Diabetes and Dyslipidemia Screening in Pediatric Practice

The incidence of diabetes and hyperlipidemia are increasing at rapid rates in children. These conditions are associated with increased risk of macrovascular and microvascular complications causing major morbidity and mortality later in life. Early diagnosis and treatment can reduce the lifelong risk of complications from these diseases, exemplifying the importance of screening in the pediatric population. The following article presents a summary of the current guidelines for diabetes and hyperlipidemia screening in pediatric patients.

Screening and Prevention of Type 1 Diabetes: Where Are We?

A diagnosis of type 1 diabetes (T1D) and the subsequent requirement for exogenous insulin treatment is associated with considerable acute and chronic morbidity and a substantial effect on patient quality of life. Importantly, a large body of work suggests that early identification of presymptomatic T1D can accurately predict clinical disease, and when paired with education and monitoring, can yield improved health outcomes. Furthermore, a growing cadre of effective disease-modifying therapies provides the potential to alter the natural history of early stages of T1D. In this mini review, we highlight prior work that has led to the current landscape of T1D screening and prevention, as well as challenges and next steps moving into the future of these rapidly evolving areas of patient care.

CXADR polymorphism rs6517774 modifies islet autoimmunity characteristics and exhibits sex disparity

Enteroviral infections have been linked to the development of islet autoimmunity (IA) and type 1 diabetes (T1D), and the coxsackie and adenovirus receptor (CXADR) is one of the ligands used by adenoviruses and enteroviruses for cell internalization. Two CXADR single nucleotide polymorphisms (SNPs), rs6517774 and rs2824404, were previously associated with an increased susceptibility to IA in the international TEDDY study (The Environmental Determinants of Diabetes in the Young). This study aimed to replicate the results by genotyping 2886 children enrolled in the Finnish Diabetes Prediction and Prevention study (DIPP). In our preliminary analysis of the SNPs' allelic distributions, we could not find any association with IA susceptibility. However, a stratified analysis revealed a sex disparity, since the allelic distribution of rs6517774 was different when comparing autoantibody positive females with males; a difference not seen in healthy subjects. By using HLA risk groups and sex as covariates, a Cox regression survival analysis found that the rs6517774 (A/G) SNP was associated with a lower age at seroconversion in females (Female*rs6517774-AA; HR = 1.53, p = 0.002), while introducing a protective effect in males. Accordingly, we propose that rs6517774 alters IA characteristics by modifying the age at seroconversion in a sex-dependent manner. In light of this observation, rs6517774 now joins a limited set on SNPs found to introduce sex-dependent risk effects on the age at IA initiation.

Preclinical models for Type 1 Diabetes Mellitus - A practical approach for research

Numerous preclinical models have been developed to advance biomedical research in type 1 diabetes mellitus (T1DM). They are essential for improving our knowledge of T1DM development and progression, allowing researchers to identify potential therapeutic targets and evaluate the effectiveness of new medications. A deeper comprehension of these models themselves is critical not only to determine the optimal strategies for their utilization but also to fully unlock their potential applications in both basic and translational research. Here, we will comprehensively summarize and discuss the applications, advantages, and limitations of the commonly used animal models for human T1DM and also overview the up-to-date human tissue bioengineering models for the investigation of T1DM. By combining these models with a better understanding of the pathophysiology of T1DM, we can enhance our insights into disease initiation and development, ultimately leading to improved therapeutic responses and outcomes.

Physical Activity and the Development of Islet Autoimmunity and Type 1 Diabetes in 5- to 15-Year-Old Children Followed in the TEDDY Study

OBJECTIVE

This study investigated physical activity and its association with the development of islet autoimmunity and type 1 diabetes in genetically at-risk children aged 5-15 years.

RESEARCH DESIGN AND METHODS

As part of the longitudinal Environmental Determinants of Diabetes in the Young (TEDDY) study, annual assessment of activity using accelerometry was conducted from age 5 years. Time-to-event analyses using Cox proportional hazard models were used to assess the association between time spent in moderate to vigorous physical activity per day and the appearance of one or several autoantibodies and progression to type 1 diabetes in three risk groups: 1) 3,869 islet autoantibody (IA)-negative children, of whom 157 became single IA positive; 2) 302 single IA-positive children, of whom 73 became multiple IA positive; and 3) 294 multiple IA-positive children, of whom 148 developed type 1 diabetes.

RESULTS

No significant association was found in risk group 1 or risk group 2. A significant association was seen in risk group 3 (hazard ratio 0.920 [95% CI 0.856, 0.988] per 10-min increase; P = 0.021), particularly when glutamate decarboxylase autoantibody was the first autoantibody (hazard ratio 0.883 [95% CI 0.783, 0.996] per 10-min increase; P = 0.043).

CONCLUSIONS

More daily minutes spent in moderate to vigorous physical activity was associated with a reduced risk of progression to type 1 diabetes in children aged 5-15 years who had developed multiple IAs.

Gene expression signature predicts rate of type 1 diabetes progression

BACKGROUND

Type 1 diabetes is a complex heterogenous autoimmune disease without therapeutic interventions available to prevent or reverse the disease. This study aimed to identify transcriptional changes associated with the disease progression in patients with recent-onset type 1 diabetes.

METHODS

Whole-blood samples were collected as part of the INNODIA study at baseline and 12 months after diagnosis of type 1 diabetes. We used linear mixed-effects modelling on RNA-seq data to identify genes associated with age, sex, or disease progression. Cell-type proportions were estimated from the RNA-seq data using computational deconvolution. Associations to clinical variables were estimated using Pearson's or point-biserial correlation for continuous and dichotomous variables, respectively, using only complete pairs of observations.

FINDINGS

We found that genes and pathways related to innate immunity were downregulated during the first year after diagnosis. Significant associations of the gene expression changes were found with ZnT8A autoantibody positivity. Rate of change in the expression of 16 genes between baseline and 12 months was found to predict the decline in C-peptide at 24 months. Interestingly and consistent with earlier reports, increased B cell levels and decreased neutrophil levels were associated with the rapid progression.

INTERPRETATION

There is considerable individual variation in the rate of progression from appearance of type 1 diabetes-specific autoantibodies to clinical disease. Patient stratification and prediction of disease progression can help in developing more personalised therapeutic strategies for different disease endotypes.

FUNDING

A full list of funding bodies can be found under Acknowledgments.

Lessons and Gaps in the Prediction and Prevention of Type 1 Diabetes

Type 1 diabetes (T1D) is a serious chronic autoimmune condition. Even though the root cause of T1D development has yet to be determined, enough is known about the natural history of T1D pathogenesis to allow study of interventions that may delay or even prevent the onset of hyperglycemia and clinical T1D. Primary prevention aims to prevent the onset of beta cell autoimmunity in asymptomatic people at high genetic risk for T1D. Secondary prevention strategies aim to preserve functional beta cells once autoimmunity is present, and tertiary prevention aims to initiate and extend partial remission of beta cell destruction after the clinical onset of T1D. The approval of teplizumab in the United States to delay the onset of clinical T1D marks an impressive milestone in diabetes care. This treatment opens the door to a paradigm shift in T1D care. People with T1D risk need to be identified early by measuring T1D related islet autoantibodies. Identifying people with T1D before they have symptoms will facilitate better understanding of pre-symptomatic T1D progression and T1D prevention strategies that may be effective.

CTLA4, SH2B3, and CLEC16A diversely affect the progression of early islet autoimmunity in relatives of Type 1 diabetes patients

The HLA region is the major genetic risk determinant of Type 1 diabetes. How non-HLA loci contribute to the genetic risk is incompletely understood, but there are indications that at least some impact progression of asymptomatic autoimmunity. We examined whether SNPs in 7 susceptibility loci (INS, SH2B3, PTPN2, PTPN22, CTLA4, CLEC16A, and IL2RA) could improve prediction of the progression from single to multiple autoantibody positivity, and from there on to diagnosis. SNPs were genotyped in persistently autoantibody positive relatives by allelic discrimination qPCR and disease progression was studied by multivariate Cox regression analysis. In our cohort, only the CTLA4 GA genotype (rs3087243, P = 0.002) and the CLEC16A AA genotype (rs12708716, P = 0.021) were associated with accelerated progression from single to multiple autoantibody positivity, but their effects were restricted to presence of HLA-DQ2/DQ8, and IAA as first autoantibody, respectively. The interaction of CTLA4 and HLA-DQ2/DQ8 overruled the effect of DQ2/DQ8 alone. The HLA-DQ2/DQ8-mediated risk of progression to multiple autoantibodies nearly entirely depended on heterozygosity for CTLA4. The SH2B3 TT genotype (rs3184504) was protective for HLA-DQ8 positive subjects (P = 0.003). At the stage of multiple autoantibodies, only the CTLA4 GA genotype was a minor independent risk factor for progression towards clinical diabetes (P = 0.034). Our study shows that non-HLA polymorphisms impact progression of islet autoimmunity in a subgroup-, stage- and SNP-specific way, suggesting distinct mechanisms. If confirmed, these findings may help refine risk assessment, follow-up, and prevention trials in risk groups.

Risk Modeling to Reduce Monitoring of an Autoantibody-Positive Population to Prevent DKA at Type 1 Diabetes Diagnosis

CONTEXT

The presence of islet autoimmunity identifies individuals likely to progress to clinical type 1 diabetes (T1D). In clinical research studies, autoantibody screening followed by regular metabolic monitoring every 6 months reduces incidence of diabetic ketoacidosis (DKA) at diagnosis.

OBJECTIVE

We hypothesized that DKA reduction can be achieved on a population basis with a reduced frequency of metabolic monitoring visits. We reasoned that prolonged time between the development of T1D and the time of clinical diagnosis ("undiagnosed time") would more commonly result in DKA and thus that limiting undiagnosed time would decrease DKA.

METHODS

An analysis was conducted of data from TrialNet's Pathway to Prevention (PTP), a cross-sectional longitudinal study that identifies and follows at-risk relatives of people with T1D. PTP is a population-based study enrolling across multiple countries. A total of 6193 autoantibody (AAB)-positive individuals participated in PTP from March 2004 to April 2019. We developed models of progression to clinical diagnosis for pediatric and adult populations with single or multiple AAB, and summarized results using estimated hazard rate. An optimal monitoring visit schedule was determined for each model to achieve a minimum average level of undiagnosed time for each population.

RESULTS

Halving the number of monitoring visits usually conducted in research studies is likely to substantially lower the population incidence of DKA at diagnosis of T1D.

CONCLUSION

Our study has clinical implications for the metabolic monitoring of at-risk individuals. Fewer monitoring visits would reduce the clinical burden, suggesting a path toward transitioning monitoring beyond the research setting.

The Association of Human Leukocyte Antigens Complex with Type 1 Diabetes in the Omani Population

Objectives

Identification of the high risk alleles, genotypes and haplotypes of the human leukocyte antigens (HLA) in different populations is beneficial for understanding their roles in type 1 diabetes (T1D) pathogenesis and intervention practices. This study aimed to identify T1D-associated HLA gene alleles in the Omani population.

Methods

The present case-control study included 73 diabetic seropositive children (mean age 9.08 ± 3.27 years) attending the paediatric clinic at Sultan Qaboos University Hospital in Muscat, Oman, and 110 healthy controls. HLA-A, -B, -C, -DRB1 and -DQB1 genes were genotyped using a sequence-specific primer polymerase chain reaction (SSP-PCR).

Results

Two HLA class I alleles (B*08, B*58) and three class II alleles (DQB1*02, DRB1*03 and DRB1*04) were associated with T1D susceptibility, while one class I (B*51) and three class II (DQB1*05, DQB1*06 and DRB1*16) alleles were associated with T1D protection. HLA-DRB1*03 and DQB1*02 alleles showed the strongest risk association among all alleles. Six DRB1 residues (E⁹, S¹¹, S¹³, Y³⁰, V⁷⁰ and K⁷¹) were significantly associated with T1D susceptibility. Heterozygous genotypes, HLA-DRB1*03/*04 and DQB1*02/*03 were significantly associated with T1D susceptibility (P <0.0001, odds ratio [OR] = 63.21 and P = 0.02, OR = 3.63, respectively). Furthermore, a significant combined action of DRB1*03-DQB1*02 haplotype in T1D risk (P = 0.000176, OR = 15) and DRB1*16-DQB1*05 haplotype in protection (P = 0.0312, OR = 0.48) was detected.

Conclusion

Known HLA class II gene alleles are associated with T1D in Omani children.

Barriers to Screening: An Analysis of Factors Impacting Screening for Type 1 Diabetes Prevention Trials

Context

Participants with stage 1 or 2 type 1 diabetes (T1D) qualify for prevention trials, but factors involved in screening for such trials are largely unknown.

Objective

To identify factors associated with screening for T1D prevention trials.

Methods

This study included TrialNet Pathway to Prevention participants who were eligible for a prevention trial: oral insulin (TN-07, TN-20), teplizumab (TN-10), abatacept (TN-18), and oral hydroxychloroquine (TN-22). Univariate and multivariate logistic regression models were used to examine participant, site, and study factors at the time of prevention trial accrual.

Results

Screening rates for trials were: 50% for TN-07 (584 screened/1172 eligible), 9% for TN-10 (106/1249), 24% for TN-18 (313/1285), 17% for TN-20 (113/667), and 28% for TN-22 (371/1336). Younger age and male sex were associated with higher screening rates for prevention trials overall and for oral therapies. Participants with an offspring with T1D showed lower rates of screening for all trials and oral drug trials compared with participants with other first-degree relatives as probands. Site factors, including larger monitoring volume and US site vs international site, were associated with higher prevention trial screening rates.

Conclusions

Clear differences exist between participants who screen for prevention trials and those who do not screen and between the research sites involved in prevention trial screening. Participant age, sex, and relationship to proband are significantly associated with prevention trial screening in addition to key site factors. Identifying these factors can facilitate strategic recruitment planning to support rapid and successful enrollment into prevention trials.

HbA1c as a time predictive biomarker for an additional islet autoantibody and type 1 diabetes in seroconverted TEDDY children

OBJECTIVE

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.

RESEARCH DESIGN AND 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 < 0.001) regardless of first appearing autoantibody, autoantibody number or type. A decrease in HbA1c levels was associated with the development of IA-2A as a second autoantibody following GADA (HR 0.85, 95% CI [0.75, 0.97], p = 0.017) and a fourth autoantibody following GADA, IAA and ZnT8A (HR 0.90, 95% CI [0.82, 0.99], p = 0.036). HbA1c trajectory analyses showed a significant increase of HbA1c over time (p < 0.001) and that the increase is more rapid as the number of autoantibodies increased from one to three (p < 0.001).

CONCLUSION

In conclusion, increased HbA1c is a reliable time predictive marker for type 1 diabetes onset. The increased rate of increase of HbA1c from first to third autoantibody and the decrease in HbA1c predicting the development of IA-2A are novel findings proving the link between HbA1c and the appearance of autoantibodies.

Predictors of the Initiation of Islet Autoimmunity and Progression to Multiple Autoantibodies and Clinical Diabetes: The TEDDY Study

OBJECTIVE

To distinguish among predictors of seroconversion, progression to multiple autoantibodies and from multiple autoantibodies to type 1 diabetes in young children.

RESEARCH DESIGN AND METHODS

Genetically high-risk newborns (n = 8,502) were followed for a median of 11.2 years (interquartile range 9.3-12.6); 835 (9.8%) developed islet autoantibodies and 283 (3.3%) were diagnosed with type 1 diabetes. Predictors were examined using Cox proportional hazards models.

RESULTS

Predictors of seroconversion and progression differed, depending on the type of first appearing autoantibody. Male sex, Finnish residence, having a sibling with type 1 diabetes, the HLA DR4 allele, probiotic use before age 28 days, and single nucleotide polymorphism (SNP) rs689_A (INS) predicted seroconversion to IAA-first (having islet autoantibody to insulin as the first appearing autoantibody). Increased weight at 12 months and SNPs rs12708716_G (CLEC16A) and rs2292239_T (ERBB3) predicted GADA-first (autoantibody to GAD as the first appearing). For those having a father with type 1 diabetes, the SNPs rs2476601_A (PTPN22) and rs3184504_T (SH2B3) predicted both. Younger age at seroconversion predicted progression from single to multiple autoantibodies as well as progression to diabetes, except for those presenting with GADA-first. Family history of type 1 diabetes and the HLA DR4 allele predicted progression to multiple autoantibodies but not diabetes. Sex did not predict progression to multiple autoantibodies, but males progressed more slowly than females from multiple autoantibodies to diabetes. SKAP2 and MIR3681HG SNPs are newly reported to be significantly associated with progression from multiple autoantibodies to type 1 diabetes.

CONCLUSIONS

Predictors of IAA-first versus GADA-first autoimmunity differ from each other and from the predictors of progression to diabetes.

Rising Hemoglobin A1c in the Nondiabetic Range Predicts Progression of Type 1 Diabetes As Well As Oral Glucose Tolerance Tests

OBJECTIVE

Biomarkers predicting risk of type 1 diabetes (stage 3) among children with islet autoantibodies are greatly needed to prevent diabetic ketoacidosis and facilitate prevention therapies.

RESEARCH DESIGN AND METHODS

Children in the prospective The Environmental Determinants of Diabetes in the Young (TEDDY) study (n = 707) with confirmed diabetes-associated autoantibodies (GAD antibody, IA-2A, and/or insulin autoantibody) and two or more HbA1c measurements were followed to diabetes or median age 11.1 years. Once confirmed autoantibody positive, HbA1c was measured quarterly. Cox models and receiver operative characteristic curve analyses revealed the prognostic utility for risk of stage 3 on a relative HbA1c increase from the baseline visit or an oral glucose tolerance test (OGTT) 2-h plasma glucose (2-hPG). This HbA1c approach was then validated in the Type 1 Diabetes TrialNet Pathway to Prevention Study (TrialNet) (n = 1,190).

RESULTS

A 10% relative HbA1c increase from baseline best marked the increased risk of stage 3 in TEDDY (74% sensitive; 88% specific). Significant predictors of risk for HbA1c change were age and HbA1c at the baseline test, genetic sex, maximum number of autoantibodies, and maximum rate of HbA1c increase by time of change. The multivariable model featuring a HbA1c ≥10% increase and these additional factors revealed increased risk of stage 3 in TEDDY (hazard ratio [HR] 12.74, 95% CI 8.7-18.6, P < 0.0001) and TrialNet (HR 5.09, 95% CI 3.3-7.9, P < 0.0001). Furthermore, the composite model using HbA1c ≥10% increase performed similarly to an OGTT 2-hPG composite model (TEDDY area under the curve [AUC] 0.88 and 0.85, respectively) and to the HbA1c model in TrialNet (AUC 0.82).

CONCLUSIONS

An increase of ≥10% in HbA1c from baseline is as informative as OGTT 2-hPG in predicting risk of stage 3 in youth with genetic risk and diabetes-associated autoantibodies.

Integration of Infant Metabolite, Genetic, and Islet Autoimmunity Signatures to Predict Type 1 Diabetes by Age 6 Years

CONTEXT

Biomarkers that can accurately predict risk of type 1 diabetes (T1D) in genetically predisposed children can facilitate interventions to delay or prevent the disease.

OBJECTIVE

This work aimed to determine if a combination of genetic, immunologic, and metabolic features, measured at infancy, can be used to predict the likelihood that a child will develop T1D by age 6 years.

METHODS

Newborns with human leukocyte antigen (HLA) typing were enrolled in the prospective birth cohort of The Environmental Determinants of Diabetes in the Young (TEDDY). TEDDY ascertained children in Finland, Germany, Sweden, and the United States. TEDDY children were either from the general population or from families with T1D with an HLA genotype associated with T1D specific to TEDDY eligibility criteria. From the TEDDY cohort there were 702 children will all data sources measured at ages 3, 6, and 9 months, 11.4% of whom progressed to T1D by age 6 years. The main outcome measure was a diagnosis of T1D as diagnosed by American Diabetes Association criteria.

RESULTS

Machine learning-based feature selection yielded classifiers based on disparate demographic, immunologic, genetic, and metabolite features. The accuracy of the model using all available data evaluated by the area under a receiver operating characteristic curve is 0.84. Reducing to only 3- and 9-month measurements did not reduce the area under the curve significantly. Metabolomics had the largest value when evaluating the accuracy at a low false-positive rate.

CONCLUSION

The metabolite features identified as important for progression to T1D by age 6 years point to altered sugar metabolism in infancy. Integrating this information with classic risk factors improves prediction of the progression to T1D in early childhood.

Non-HLA Gene Polymorphisms in the Pathogenesis of Type 1 Diabetes: Phase and Endotype Specific Effects

The non-HLA loci conferring susceptibility to type 1 diabetes determine approximately half of the genetic disease risk, and several of them have been shown to affect immune-cell or pancreatic β-cell functions. A number of these loci have shown associations with the appearance of autoantibodies or with progression from seroconversion to clinical type 1 diabetes. In the current study, we have re-analyzed 21 of our loci with prior association evidence using an expanded DIPP follow-up cohort of 976 autoantibody positive cases and 1,910 matched controls. Survival analysis using Cox regression was applied for time periods from birth to seroconversion and from seroconversion to type 1 diabetes. The appearance of autoantibodies was also analyzed in endotypes, which are defined by the first appearing autoantibody, either IAA or GADA. Analyzing the time period from birth to seroconversion, we were able to replicate our previous association findings at PTPN22, INS, and NRP1. Novel findings included associations with ERBB3, UBASH3A, PTPN2, and FUT2. In the time period from seroconversion to clinical type 1 diabetes, prior associations with PTPN2, CD226, and PTPN22 were replicated, and a novel association with STAT4 was observed. Analyzing the appearance of autoantibodies in endotypes, the PTPN22 association was specific for IAA-first. In the progression phase, STAT4 was specific for IAA-first and ERBB3 to GADA-first. In conclusion, our results further the knowledge of the function of non-HLA risk polymorphisms in detailing endotype specificity and timing of disease development.

Characterising the age-dependent effects of risk factors on type 1 diabetes progression

AIMS/HYPOTHESIS

Age is known to be one of the most important stratifiers of disease progression in type 1 diabetes. However, what drives the difference in rate of progression between adults and children is poorly understood. Evidence suggests that many type 1 diabetes disease predictors do not have the same effect across the age spectrum. Without a comprehensive analysis describing the varying risk profiles of predictors over the age continuum, researchers and clinicians are susceptible to inappropriate assessment of risk when examining populations of differing ages. We aimed to systematically assess and characterise how the effect of key type 1 diabetes risk predictors changes with age.

METHODS

Using longitudinal data from single- and multiple-autoantibody-positive at-risk individuals recruited between the ages of 1 and 45 years in TrialNet's Pathway to Prevention Study, we assessed and visually characterised the age-varying effect of key demographic, immune and metabolic predictors of type 1 diabetes by employing a flexible spline model. Two progression outcomes were defined: participants with single autoantibodies (n=4893) were analysed for progression to multiple autoantibodies or type 1 diabetes, and participants with multiple autoantibodies were analysed (n=3856) for progression to type 1 diabetes.

RESULTS

Several predictors exhibited significant age-varying effects on disease progression. Amongst single-autoantibody participants, HLA-DR3 (p=0.007), GAD65 autoantibody positivity (p=0.008), elevated BMI (p=0.007) and HOMA-IR (p=0.002) showed a significant increase in effect on disease progression with increasing age. Insulin autoantibody positivity had a diminishing effect with older age in single-autoantibody-positive participants (p<0.001). Amongst multiple-autoantibody-positive participants, male sex (p=0.002) was associated with an increase in risk for progression, and HLA DR3/4 (p=0.05) showed a decreased effect on disease progression with older age. In both single- and multiple-autoantibody-positive individuals, significant changes in HR with age were seen for multiple measures of islet function. Risk estimation using prediction risk score Index60 was found to be better at a younger age for both single- and multiple-autoantibody-positive individuals (p=0.007 and p<0.001, respectively). No age-varying effect was seen for prediction risk score DPTRS (p=0.861 and p=0.178, respectively). Multivariable analyses suggested that incorporating the age-varying effect of the individual components of these validated risk scores has the potential to enhance the risk estimate.

CONCLUSIONS/INTERPRETATION

Analysing the age-varying effect of disease predictors improves understanding and prediction of type 1 diabetes disease progression, and should be leveraged to refine prediction models and guide mechanistic studies.

Heterogeneity in the presentation of clinical type 1 diabetes defined by the level of risk conferred by human leukocyte antigen class II genotypes

OBJECTIVES

The association between human leukocyte antigen (HLA) class II genotypes and susceptibility to type 1 diabetes (T1D) is well established. This study aimed at examining whether there are differences in the presentation of T1D depending on the HLA genotype.

RESEARCH DESIGN AND METHODS

We divided the study participants (N = 5798) in the Finnish Pediatric Diabetes Register into two groups based on the T1D risk conferred by their HLA genotype (high and moderate-risk genotypes, Group 1 vs. other genotypes, Group 2). We then examined differences in clinical, metabolic, and immunological characteristics. Children included in the study were 0-14-year-old and diagnosed between January 2003 and December 2019.

RESULTS

Participants in Group 1 were younger at the time of diagnosis (P < 0.001) and had more frequently family members affected by T1D (P < 0.001). Diabetic ketoacidosis (DKA) was more frequent among participants in Group 2 (P = 0.014) who also had a longer duration of symptoms before diagnosis (P < 0.001) and higher hemoglobin A1c (P = 0.001) at diagnosis. The HLA genotype was not, however, directly related to the DKA frequency. The frequency of islet cell antibodies (P < 0.003), insulin autoantibodies (P < 0.001), and islet antigen 2 autoantibodies (P < 0.001) was higher in Group 1 whereas glutamic acid decarboxylase autoantibodies were more frequent (P < 0.001) in Group 2. Group 1 had more participants with multiple autoantibodies (P = 0.027) whereas antibody negativity was more frequent in Group 2 (P = 0.003).

CONCLUSIONS

These findings indicate disease heterogeneity in relation to both clinical disease presentation and humoral autoimmunity, in particular. This heterogeneity is, at least partly, defined by HLA Class II genotypes.

Modeling human T1D-associated autoimmune processes

BACKGROUND

Type 1 diabetes (T1D) is an autoimmune disease characterized by impaired immune tolerance to β-cell antigens and progressive destruction of insulin-producing β-cells. Animal models have provided valuable insights for understanding the etiology and pathogenesis of this disease, but they fall short of reflecting the extensive heterogeneity of the disease in humans, which is contributed by various combinations of risk gene alleles and unique environmental factors. Collectively, these factors have been used to define subgroups of patients, termed endotypes, with distinct predominating disease characteristics.

SCOPE OF REVIEW

Here, we review the gaps filled by these models in understanding the intricate involvement and regulation of the immune system in human T1D pathogenesis. We describe the various models developed so far and the scientific questions that have been addressed using them. Finally, we discuss the limitations of these models, primarily ascribed to hosting a human immune system (HIS) in a xenogeneic recipient, and what remains to be done to improve their physiological relevance.

MAJOR CONCLUSIONS

To understand the role of genetic and environmental factors or evaluate immune-modifying therapies in humans, it is critical to develop and apply models in which human cells can be manipulated and their functions studied under conditions that recapitulate as closely as possible the physiological conditions of the human body. While microphysiological systems and living tissue slices provide some of these conditions, HIS mice enable more extensive analyses using in vivo systems.

Immunotherapy for type 1 diabetes

INTRODUCTION

Despite advances in technology including the development of more sophisticated methods of monitoring blood glucose and delivering insulin, many individuals with type 1 diabetes continue to experience significant challenges in optimizing glycaemic control. Alternative treatment approaches to insulin are required. Increasing efforts have focused on developing treatments aimed at targeting the underlying disease process to modulate the immune system, maximize beta cell function and enhance endogenous insulin production and action.

SOURCES OF DATA

Literature searches with keywords 'Type 1 diabetes and immunotherapy', publications relating to clinical trials of immunotherapy in type 1 diabetes.

AREAS OF AGREEMENT

Insulin therapy is insufficient to achieve optimal glycaemic control in many individuals with type 1 diabetes, and new treatment approaches are required. Studies have showed promising results for the use of immunotherapy as a means of delaying disease onset and progression.

AREAS OF CONTROVERSY

The optimal way of identifying individuals most likely to benefit from immunotherapies.

GROWING POINTS

A better understanding of the natural history of type 1 diabetes has made it possible to identify individuals who have developed autoimmunity but have not yet progressed to clinical diabetes, offering opportunities not only to develop treatments that delay disease progression, but prevent its development in the first place. A consensus on how to identify individuals who may benefit from immunotherapy to prevent disease onset is needed.

AREAS TIMELY FOR DEVELOPING RESEARCH

The development of optimal strategies for preventing and delaying progression of type 1 diabetes, and monitoring the response to immunointervention.

Sex-dependent effects on the gut microbiota and host metabolome in type 1 diabetic mice

Sexual dimorphism exists in the onset and development of type 1 diabetes (T1D), but its potential pathological mechanism is poorly understood. In the present study, we examined sex-specific changes in the gut microbiome and host metabolome of T1D mice via 16S rRNA gene sequencing and nuclear magnetic resonance (NMR)-based metabolomics approach, and aimed to investigate potential mechanism of the gut microbiota-host metabolic interaction in the sexual dimorphism of T1D. Our results demonstrate that female mice had a greater shift in the gut microbiota than male mice during the development of T1D; however, host metabolome was more susceptible to T1D in male mice. The correlation network analysis indicates that T1D-induced host metabolic changes may be regulated by the gut microbiota in a sex-specific manner, mainly involving short-chain fatty acids (SCFAs) metabolism, energy metabolism, amino acid metabolism, and choline metabolism. Therefore, our study suggests that sex-dependent "gut microbiota-host metabolism axis" may be implicated in the sexual dimorphism of T1D, and the link between microbes and metabolites might contribute to the prevention and treatment of T1D.

Diabetes type 1: Can it be treated as an autoimmune disorder?

Type 1 Diabetes Mellitus (T1DM) is characterized by progressive autoimmune-mediated destruction of the pancreatic beta-cells leading to insulin deficiency and hyperglycemia. It is associated with significant treatment burden and necessitates life-long insulin therapy. The role of immunotherapy in the prevention and management of T1DM is an evolving area of interest which has the potential to alter the natural history of this disease.In this review, we give insight into recent clinical trials related to the use of immunotherapeutic approaches for T1DM, such as proinflammatory cytokine inhibition, cell-depletion and cell-therapy approaches, autoantigen-specific treatments and stem cell therapies. We highlight the timing of intervention, aspects of therapy including adverse effects and the emergence of a novel lymphocyte crucial in T1DM autoimmunity. We also discuss the role of cardiac autoimmunity and its link to excess CVD risk in T1DM.We conclude that significant advances have been made in development of immunotherapeutic targets and agents for the treatment and prevention of T1DM. These immune-based therapies promise preservation of beta-cells and decreasing insulin dependency. In their current state, immunotherapeutic approaches cannot yet halt the progression from a preclinical state to overt T1DM nor can they replace standard insulin therapy in existing T1DM. It remains to be seen whether immunotherapy will ultimately play a key role in the prevention of progression to overt T1DM and whether it may find a place in our therapeutic armamentarium to improve clinical outcomes and quality of life in established T1DM.

Dynamic changes in immune gene co-expression networks predict development of type 1 diabetes

Significant progress has been made in elucidating genetic risk factors influencing Type 1 diabetes (T1D); however, features other than genetic variants that initiate and/or accelerate islet autoimmunity that lead to the development of clinical T1D remain largely unknown. We hypothesized that genetic and environmental risk factors can both contribute to T1D through dynamic alterations of molecular interactions in physiologic networks. To test this hypothesis, we utilized longitudinal blood transcriptomic profiles in The Environmental Determinants of Diabetes in the Young (TEDDY) study to generate gene co-expression networks. In network modules that contain immune response genes associated with T1D, we observed highly dynamic differences in module connectivity in the 600 days (~ 2 years) preceding clinical diagnosis of T1D. Our results suggest that gene co-expression is highly plastic and that connectivity differences in T1D-associated immune system genes influence the timing and development of clinical disease.

Simplifying prediction of disease progression in pre-symptomatic type 1 diabetes using a single blood sample

AIMS/HYPOTHESIS

Accurate prediction of disease progression in individuals with pre-symptomatic type 1 diabetes has potential to prevent ketoacidosis and accelerate development of disease-modifying therapies. Current tools for predicting risk require multiple blood samples taken during an OGTT. Our aim was to develop and validate a simpler tool based on a single blood draw.

METHODS

Models to predict disease progression using a single OGTT time point (0, 30, 60, 90 or 120 min) were developed using TrialNet data collected from relatives with type 1 diabetes and validated in independent populations at high genetic risk of type 1 diabetes (TrialNet, Diabetes Prevention Trial-Type 1, The Environmental Determinants of Diabetes in the Young [1]) and in a general population of Bavarian children who participated in Fr1da.

RESULTS

Cox proportional hazards models combining plasma glucose, C-peptide, sex, age, BMI, HbA_1c and insulinoma antigen-2 autoantibody status predicted disease progression in all populations. In TrialNet, the AUC for receiver operating characteristic curves for models named M₆₀, M₉₀ and M₁₂₀, based on sampling at 60, 90 and 120 min, was 0.760, 0.761 and 0.745, respectively. These were not significantly different from the AUC of 0.760 for the gold standard Diabetes Prevention Trial Risk Score, which requires five OGTT blood samples. In TEDDY, where only 120 min blood sampling had been performed, the M₁₂₀ AUC was 0.865. In Fr1da, the M₁₂₀ AUC of 0.742 was significantly greater than the M₆₀ AUC of 0.615.

CONCLUSIONS/INTERPRETATION

Prediction models based on a single OGTT blood draw accurately predict disease progression from stage 1 or 2 to stage 3 type 1 diabetes. The operational simplicity of M₁₂₀, its validity across different at-risk populations and the requirement for 120 min sampling to stage type 1 diabetes suggest M₁₂₀ could be readily applied to decrease the cost and complexity of risk stratification.

Genetic variation at ERBB3/IKZF4 and sexual dimorphism in epitope spreading in single autoantibody-positive relatives

AIMS/HYPOTHESIS

We examined whether the non-HLA susceptibility locus ERBB3/IKZF4 influences progression of type 1 diabetes stage specifically according to sex.

METHODS

SNPs of ERBB3 (rs2292239 T/G) and IKZF4 (rs1701704 G/T) were screened by allelic discrimination quantitative PCR assay in first-degree relatives of type 1 diabetes patients who had developed at least one circulating autoantibody. The effect of ERBB3/IKZF4 genotypes and sex, on the progression of single autoantibody positivity to multiple autoantibody positivity and from multiple autoantibody positivity to diabetes, was studied by Kaplan-Meier analysis and multivariate Cox regression.

RESULTS

In the cohort of autoantibody-positive first-degree relatives, the risk allele frequencies for ERBB3 rs2292239 (T) and IKZF4 rs1701704 (G) were increased. There was a significant male excess at the stage of multiple autoantibody positivity (p = 0.021). In Kaplan-Meier survival analysis, progression from single to multiple antibody positivity was delayed in female participants with genotype ERBB3 GG (p = 0.018, vs ERBB3 TG+TT) or IKZF4 TT (p = 0.023, vs IKZF4 GT+GG), but not in male participants. In multivariate Cox regression models, the interaction effects between female sex and ERBB3 GG (p = 0.012; HR = 0.305 [95% CI 0.120, 0.773]) or between female sex and IKZF4 TT (p = 0.011; HR = 0.329 [95% CI 0.140, 0.777]) emerged as potential determinants of delayed progression to multiple autoantibodies. The progression from multiple autoantibody positivity to type 1 diabetes appeared not to be influenced by ERBB3/IKZF4.

CONCLUSIONS/INTERPRETATION

In siblings and offspring of type 1 diabetes patients, polymorphism in region ERBB3/IKZF4 may affect disease progression at the level of epitope spreading in female individuals. Our findings suggest that interaction between sex and ERBB3/IKZF4 may contribute to the post-pubertal male excess in type 1 diabetes.

The β Cell in Diabetes: Integrating Biomarkers With Functional Measures

The pathogenesis of hyperglycemia observed in most forms of diabetes is intimately tied to the islet β cell. Impairments in propeptide processing and secretory function, along with the loss of these vital cells, is demonstrable not only in those in whom the diagnosis is established but typically also in individuals who are at increased risk of developing the disease. Biomarkers are used to inform on the state of a biological process, pathological condition, or response to an intervention and are increasingly being used for predicting, diagnosing, and prognosticating disease. They are also proving to be of use in the different forms of diabetes in both research and clinical settings. This review focuses on the β cell, addressing the potential utility of genetic markers, circulating molecules, immune cell phenotyping, and imaging approaches as biomarkers of cellular function and loss of this critical cell. Further, we consider how these biomarkers complement the more long-established, dynamic, and often complex measurements of β-cell secretory function that themselves could be considered biomarkers.

Frailty modeling under a selective sampling protocol: an application to type 1 diabetes related autoantibodies

In studies following selective sampling protocols for secondary outcomes, conventional analyses regarding their appearance could provide misguided information. In the large type 1 diabetes prevention and prediction (DIPP) cohort study monitoring type 1 diabetes-associated autoantibodies, we propose to model their appearance via a multivariate frailty model, which incorporates a correlation component that is important for unbiased estimation of the baseline hazards under the selective sampling mechanism. As further advantages, the frailty model allows for systematic evaluation of the association and the differences in regression parameters among the autoantibodies. We demonstrate the properties of the model by a simulation study and the analysis of the autoantibodies and their association with background factors in the DIPP study, in which we found that high genetic risk is associated with the appearance of all the autoantibodies, whereas the association with sex and urban municipality was evident for IA-2A and IAA autoantibodies.

Neutralizing Ljungan virus antibodies in children with newly diagnosed type 1 diabetes

Ljungan virus (LV), a Parechovirus of the Picornavirus family, first isolated from a bank vole at the Ljungan river in Sweden, has been implicated in the risk for autoimmune type 1 diabetes. An assay for neutralizing Ljungan virus antibodies (NLVA) was developed using the original 87-012 LV isolate. The goal was to determine NLVA titres in incident 0-18 years old newly diagnosed type 1 diabetes patients (n=67) and school children controls (n=292) from Jämtland county in Sweden. NLVA were found in 41 of 67 (61 %) patients compared to 127 of 292 (44 %) controls (P=0.009). In the type 1 diabetes patients, NLVA titres were associated with autoantibodies to glutamic acid decarboxylase (GADA) (P=0.023), but not to autoantibodies against insulin (IAA) or islet antigen-2 (IA-2A). The NLVA assay should prove useful for further investigations to determine levels of LV antibodies in patients and future studies to determine a possible role of LV in autoimmune type 1 diabetes.

Maternal food consumption during late pregnancy and offspring risk of islet autoimmunity and type 1 diabetes

AIMS/HYPOTHESIS

We aimed to investigate the association between maternal consumption of gluten-containing foods and other selected foods during late pregnancy and offspring risk of islet autoimmunity (IA) and type 1 diabetes in The Environmental Determinants of Diabetes in the Young (TEDDY) study.

METHODS

The TEDDY study recruited children at high genetic risk for type 1 diabetes at birth, and prospectively follows them for the development of IA and type 1 diabetes (n = 8556). A questionnaire on the mother's diet in late pregnancy was completed by 3-4 months postpartum. The maternal daily intake was estimated from a food frequency questionnaire for eight food groups: gluten-containing foods, non-gluten cereals, fresh milk, sour milk, cheese products, soy products, lean/medium-fat fish and fatty fish. For each food, we described the distribution of maternal intake among the four participating countries in the TEDDY study and tested the association of tertile of maternal food consumption with risk of IA and type 1 diabetes using forward selection time-to-event Cox regression.

RESULTS

By 28 February 2019, 791 cases of IA and 328 cases of type 1 diabetes developed in TEDDY. There was no association between maternal late-pregnancy consumption of gluten-containing foods or any of the other selected foods and risk of IA, type 1 diabetes, insulin autoantibody-first IA or GAD autoantibody-first IA (all p ≥ 0.01). Maternal gluten-containing food consumption in late pregnancy was higher in Sweden (242 g/day), Germany (247 g/day) and Finland (221 g/day) than in the USA (199 g/day) (pairwise p < 0.05).

CONCLUSIONS/INTERPRETATION

Maternal food consumption during late pregnancy was not associated with offspring risk for IA or type 1 diabetes.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00279318.

Etiology of Autoimmune Islet Disease: Timing Is Everything

Life is about timing. -Carl LewisThe understanding of autoimmune type 1 diabetes is increasing, and examining etiology separate from pathogenesis has become crucial. The components to explain type 1 diabetes development have been known for some time. The strong association with HLA has been researched for nearly 50 years. Genome-wide association studies added another 60+ non-HLA genetic factors with minor contribution to risk. Insulitis has long been known to be present close to clinical diagnosis. T and B cells recognizing β-cell autoantigens are detectable prior to diagnosis and in newly diagnosed patients. Islet autoantibody tests against four major autoantigens have been standardized and used as biomarkers of islet autoimmunity. However, to clarify the etiology would require attention to time. Etiology may be defined as the cause of a disease (i.e., type 1 diabetes) or abnormal condition (i.e., islet autoimmunity). Timing is everything, as neither the prodrome of islet autoimmunity nor the clinical onset of type 1 diabetes tells us much about the etiology. Rather, the islet autoantibody that appears first and persists would mark the diagnosis of an autoimmune islet disease (AID). Events after the diagnosis of AID would represent the pathogenesis. Several islet autoantibodies without (stage 1) or with impaired glucose tolerance (stage 2) or with symptoms (stage 3) would define the pathogenesis culminating in clinical type 1 diabetes. Etiology would be about the timing of events that take place before the first-appearing islet autoantibody.

Islet Autoimmunity and HLA Markers of Presymptomatic and Clinical Type 1 Diabetes: Joint Analyses of Prospective Cohort Studies in Finland, Germany, Sweden, and the U.S

OBJECTIVE

To combine prospective cohort studies, by including HLA harmonization, and estimate risk of islet autoimmunity and progression to clinical diabetes.

RESEARCH DESIGN AND METHODS

For prospective cohorts in Finland, Germany, Sweden, and the U.S., 24,662 children at increased genetic risk for development of islet autoantibodies and type 1 diabetes have been followed. Following harmonization, the outcomes were analyzed in 16,709 infants-toddlers enrolled by age 2.5 years.

RESULTS

In the infant-toddler cohort, 1,413 (8.5%) developed at least one autoantibody confirmed at two or more consecutive visits (seroconversion), 865 (5%) developed multiple autoantibodies, and 655 (4%) progressed to diabetes. The 15-year cumulative incidence of diabetes varied in children with one, two, or three autoantibodies at seroconversion: 45% (95% CI 40-52), 85% (78-90), and 92% (85-97), respectively. Among those with a single autoantibody, status 2 years after seroconversion predicted diabetes risk: 12% (10-25) if reverting to autoantibody negative, 30% (20-40) if retaining a single autoantibody, and 82% (80-95) if developing multiple autoantibodies. HLA-DR-DQ affected the risk of confirmed seroconversion and progression to diabetes in children with stable single-autoantibody status. Their 15-year diabetes incidence for higher- versus lower-risk genotypes was 40% (28-50) vs. 12% (5-38). The rate of progression to diabetes was inversely related to age at development of multiple autoantibodies, ranging from 20% per year to 6% per year in children developing multipositivity in ≤2 years or >7.4 years, respectively.

CONCLUSIONS

The number of islet autoantibodies at seroconversion reliably predicts 15-year type 1 diabetes risk. In children retaining a single autoantibody, HLA-DR-DQ genotypes can further refine risk of progression.

A Key to T1D Prevention: Screening and Monitoring Relatives as Part of Clinical Care

The 2019 report of a randomized, placebo-controlled clinical trial demonstrating that immune therapy can delay the onset of clinical type 1 diabetes (T1D) in antibody-positive relatives by a median of 2 years stands out as a landmark in the decades-long effort to prevent T1D. With this important step achieved, it is now time to consider what is needed to bring disease-modifying therapy for prevention or delay of T1D to clinical use from this point. Long considered a chicken and egg problem (why screen for T1D risk when we have no therapy, and how can we develop therapies without more screening), we now have the opportunity to break this impasse. The purpose of this article is to place this clinical trial result in context, highlighting key foundational studies leading to this accomplishment, addressing the current gaps, and suggesting that a key next step for prevention of T1D is to screen and monitor relatives for T1D risk in the context of clinical care.

Transcriptional networks in at-risk individuals identify signatures of type 1 diabetes progression

Type 1 diabetes (T1D) is a disease of insulin deficiency that results from autoimmune destruction of pancreatic islet β cells. The exact cause of T1D remains unknown, although asymptomatic islet autoimmunity lasting from weeks to years before diagnosis raises the possibility of intervention before the onset of clinical disease. The number, type, and titer of islet autoantibodies are associated with long-term disease risk but do not cause disease, and robust early predictors of individual progression to T1D onset remain elusive. The Environmental Determinants of Diabetes in the Young (TEDDY) consortium is a prospective cohort study aiming to determine genetic and environmental interactions causing T1D. Here, we analyzed longitudinal blood transcriptomes of 2013 samples from 400 individuals in the TEDDY study before both T1D and islet autoimmunity. We identified and interpreted age-associated gene expression changes in healthy infancy and age-independent changes tracking with progression to both T1D and islet autoimmunity, beginning before other evidence of islet autoimmunity was present. We combined multivariate longitudinal data in a Bayesian joint model to predict individual risk of T1D onset and validated the association of a natural killer cell signature with progression and the model's predictive performance on an additional 356 samples from 56 individuals in the independent Type 1 Diabetes Prediction and Prevention study. Together, our results indicate that T1D is characterized by early and longitudinal changes in gene expression, informing the immunopathology of disease progression and facilitating prediction of its course.

Insulitis in the pancreas of non-diabetic organ donors under age 25 years with multiple circulating autoantibodies against islet cell antigens

Autoantibodies against islet cell antigens are routinely used to identify subjects at increased risk of symptomatic type 1 diabetes, but their relation to the intra-islet pathogenetic process that leads to positivity for these markers is poorly understood. We screened 556 non-diabetic organ donors (3 months to 24 years) for five different autoantibodies and found positivity in 27 subjects, 25 single- and two double autoantibody-positive donors. Histopathological screening of pancreatic tissue samples showed lesion characteristic for recent-onset type 1 diabetes in the two organ donors with a high-risk profile, due to their positivity for multiple autoantibodies and HLA-inferred risk. Inflammatory infiltrates (insulitis) were found in a small fraction of islets (<5%) and consisted predominantly of CD3+CD8+ T-cells. Islets with insulitis were found in close proximity to islets devoid of insulin-positivity; such pseudo-atrophic islets were present in multiple small foci scattered throughout the pancreatic tissue or were found to be distributed with a lobular pattern. Relative beta cell area in both single and multiple autoantibody-positive donors was comparable to that in autoantibody-negative controls. In conclusion, in organ donors under age 25 years, insulitis and pseudo-atrophic islets were restricted to multiple autoantibody-positive individuals allegedly at high risk of developing symptomatic type 1 diabetes, in line with reports in older age groups. These observations may give further insight into the early pathogenetic events that may culminate in clinically overt disease.

Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers

BACKGROUND

The Environmental Determinants of the Diabetes in the Young (TEDDY) study has prospectively followed, from birth, children at increased genetic risk of type 1 diabetes. TEDDY has collected heterogenous data longitudinally to gain insights into the environmental and biological mechanisms driving the progression to persistent islet autoantibodies.

METHODS

We developed a machine learning model to predict imminent transition to the development of persistent islet autoantibodies based on time-varying metabolomics data integrated with time-invariant risk factors (eg, gestational age). The machine learning was initiated with 221 potential features (85 genetic, 5 environmental, 131 metabolomic) and an ensemble-based feature evaluation was utilized to identify a small set of predictive features that can be interrogated to better understand the pathogenesis leading up to persistent islet autoimmunity.

RESULTS

The final integrative machine learning model included 42 disparate features, returning a cross-validated receiver operating characteristic area under the curve (AUC) of 0.74 and an AUC of ~0.65 on an independent validation dataset. The model identified a principal set of 20 time-invariant markers, including 18 genetic markers (16 single nucleotide polymorphisms [SNPs] and two HLA-DR genotypes) and two demographic markers (gestational age and exposure to a prebiotic formula). Integration with the metabolome identified 22 supplemental metabolites and lipids, including adipic acid and ceramide d42:0, that predicted development of islet autoantibodies.

CONCLUSIONS

The majority (86%) of metabolites that predicted development of islet autoantibodies belonged to three pathways: lipid oxidation, phospholipase A2 signaling, and pentose phosphate, suggesting that these metabolic processes may play a role in triggering islet autoimmunity.

Association between family history, early growth and the risk of beta cell autoimmunity in children at risk for type 1 diabetes

AIMS/HYPOTHESIS

The aim of this work was to examine the relationship between family history of type 1 diabetes, birthweight, growth during the first 2 years and development of multiple beta cell autoantibodies in children with a first-degree relative with type 1 diabetes and HLA-conferred disease susceptibility.

METHODS

In a secondary analysis of the Trial to Reduce IDDM in the Genetically at Risk (TRIGR), clinical characteristics and development of beta cell autoantibodies were compared in relation to family history of type 1 diabetes (mother vs father vs sibling) in 2074 children from families with a single affected family member.

RESULTS

Multiple autoantibodies (≥2 of 5 measured) developed in 277 (13%) children: 107 (10%), 114 (16%) and 56 (18%) born with a mother, father or sibling with type 1 diabetes, respectively (p < 0.001). The HR for time to multiple autoimmunity was 0.54 (95% CI 0.39, 0.75) in offspring of affected mothers (n = 107/1046, p < 0.001) and 0.81 (95% CI 0.59, 1.11) (n = 114/722, p = 0.19) in offspring of affected fathers, compared with participants with a sibling with type 1 diabetes (comparator group n = 56/306). The time to the first autoantibody present (to insulin, GAD, tyrosine phosphatase-related insulinoma-associated 2 molecules, islet cell or zinc transporter 8) was similar in the three groups. Height velocity (z score/year) in the first 24 months was independently associated with developing multiple antibodies in the total cohort (HR 1.31 [95% CI 1.01, 1.70], p = 0.04). A higher birthweight in children born to an affected mother vs affected father or an affected sibling was not related to the risk of multiple autoimmunity.

CONCLUSIONS/INTERPRETATION

The risk of developing multiple autoantibodies was lower in children with maternal type 1 diabetes. For the whole group, this risk of developing multiple autoantibodies was independent of birthweight but was greater in those with increased height velocity during the first 2 years of life. However, the risk associated with paternal type 1 diabetes was not linked to differences in birthweight or early growth.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00179777 Graphical abstract.

Characterization of plasma lipidomics in adolescent subjects with increased risk for type 1 diabetes in the DiPiS cohort

INTRODUCTION

Type 1 diabetes (T1D) is caused by the destruction of pancreatic islet beta cells resulting in total loss of insulin production. Recent studies have suggested that the destruction may be interrelated to plasma lipids.

OBJECTIVES

Specific lipids have previously been shown to be decreased in children who develop T1D before four years of age. Disturbances of plasma lipids prior to clinical diagnosis of diabetes, if true, may provide a novel way to improve prediction, and monitor disease progression.

METHODS

A lipidomic approach was utilized to analyze plasma from 67 healthy adolescent subjects (10-15 years of age) with or without islet autoantibodies but all with increased genetic risk for T1D. The study subjects were enrolled at birth in the Diabetes Prediction in Skåne (DiPiS) study and after 10-15 years of follow-up we performed the present cross-sectional analysis. HLA-DRB345, -DRB1, -DQA1, -DQB1, -DPA1 and -DPB1 genotypes were determined using next generation sequencing. Lipidomic profiles were determined using ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry. Lipidomics data were analyzed according to genotype.

RESULTS

Variation in levels of several specific phospholipid species were related to level of autoimmunity but not development of T1D. Five glycosylated ceramides were increased in insulin autoantibody (IAA) positive adolescent subjects compared to adolescent subjects without this autoantibody. Additionally, HLA genotypes seemed to influence levels of long chain triacylglycerol (TG).

CONCLUSION

Lipidomic profiling of adolescent subjects in high risk of T1D may improve sub-phenotyping in this high risk population.

Efficacy of GAD-alum immunotherapy associated with HLA-DR3-DQ2 in recently diagnosed type 1 diabetes

AIMS/HYPOTHESIS

The aim of this study was to determine if retention of C-peptide following immunotherapy using recombinant GAD65 conjugated to aluminium hydroxide (GAD-alum) is influenced by HLA risk haplotypes DR3-DQ2 and DR4-DQ8.

METHODS

HLA-dependent treatment effect of GAD-alum therapy on C-peptide retention in individuals with recent-onset type 1 diabetes was evaluated using individual-level patient data from three placebo-controlled, randomised clinical trials using a mixed repeated measures model.

RESULTS

A significant and dose-dependent effect was observed in individuals positive for the genotypes that include HLA-DR3-DQ2 but not HLA-DR4-DQ8 and in the broader subgroup of individuals positive for all genotypes that include HLA-DR3-DQ2 (i.e. including those also positive for HLA-DR4-DQ8). Higher doses (three or four injections) showed a treatment effect ratio of 1.596 (95% CI 1.132, 2.249; adjusted p = 0.0035) and 1.441 (95% CI 1.188, 1.749; adjusted p = 0.0007) vs placebo for the two respective HLA subgroups.

CONCLUSIONS/INTERPRETATION

GAD65-specific immunotherapy has a significant effect on C-peptide retention in individuals with recent-onset type 1 diabetes who have the DR3-DQ2 haplotype. Graphical abstract.

Motifs of Three HLA-DQ Amino Acid Residues (α44, β57, β135) Capture Full Association With the Risk of Type 1 Diabetes in DQ2 and DQ8 Children

HLA-DQA1 and -DQB1 are strongly associated with type 1 diabetes (T1D), and DQ8.1 and DQ2.5 are major risk haplotypes. Next-generation targeted sequencing of HLA-DQA1 and -DQB1 in Swedish newly diagnosed 1- to 18 year-old patients (n = 962) and control subjects (n = 636) was used to construct abbreviated DQ haplotypes, converted into amino acid (AA) residues, and assessed for their associations with T1D. A hierarchically organized haplotype (HOH) association analysis allowed 45 unique DQ haplotypes to be categorized into seven clusters. The DQ8/9 cluster included two DQ8.1 risk and the DQ9 resistant haplotypes, and the DQ2 cluster included the DQ2.5 risk and DQ2.2 resistant haplotypes. Within each cluster, HOH found residues α44Q (odds ratio [OR] 3.29, P = 2.38 * 10^-85) and β57A (OR 3.44, P = 3.80 * 10^-84) to be associated with T1D in the DQ8/9 cluster representing all ten residues (α22, α23, α44, α49, α51, α53, α54, α73, α184, β57) due to complete linkage disequilibrium (LD) of α44 with eight such residues. Within the DQ2 cluster and due to LD, HOH analysis found α44C and β135D to share the risk for T1D (OR 2.10, P = 1.96 * 10^-20). The motif "QAD" of α44, β57, and β135 captured the T1D risk association of DQ8.1 (OR 3.44, P = 3.80 * 10^-84), and the corresponding motif "CAD" captured the risk association of DQ2.5 (OR 2.10, P = 1.96 * 10^-20). Two risk associations were related to GAD65 autoantibody (GADA) and IA-2 autoantibody (IA-2A) but in opposite directions. CAD was positively associated with GADA (OR 1.56, P = 6.35 * 10^-8) but negatively with IA-2A (OR 0.59, P = 6.55 * 10^-11). QAD was negatively associated with GADA (OR 0.88; P = 3.70 * 10^-3) but positively with IA-2A (OR 1.64; P = 2.40 * 10^-14), despite a single difference at α44. The residues are found in and around anchor pockets 1 and 9, as potential T-cell receptor contacts, in the areas for CD4 binding and putative homodimer formation. The identification of three HLA-DQ AAs (α44, β57, β135) conferring T1D risk should sharpen functional and translational studies.

Haploinsufficiency of the NF1 gene is associated with protection against diabetes

Background

The hereditary predisposition to diabetes is only partially explained by genes identified so far. Neurofibromatosis type 1 (NF1) is a rare monogenic dominant syndrome caused by aberrations of the NF1 gene. Here, we used a cohort of 1410 patients with NF1 to study the association of the NF1 gene with type 1 (T1D) and type 2 diabetes (T2D).

Methods

A total of 1410 patients were confirmed to fulfil the National Institutes of Health diagnostic criteria for NF1 by individually reviewing their medical records. The patients with NF1 were compared with 14 017 controls matched for age, sex and area of residence as well as 1881 non-NF1 siblings of the patients with NF1. Register-based information on purchases of antidiabetic medication and hospital encounters related to diabetes were retrieved. The Cox proportional hazards model was used to calculate the relative risk for diabetes in NF1.

Results

Patients with NF1 showed a lower rate of T2D when compared with a 10-fold control cohort (HR 0.27, 95% CI 0.17 to 0.43) or with their siblings without NF1 (HR 0.28, 95% CI 0.16 to 0.47). The estimates remained practically unchanged after adjusting the analyses for history of obesity and dyslipidaemias. The rate of T1D in NF1 was decreased although statistically non-significantly (HR 0.58, 95% CI 0.27 to 1.25).

Conclusion

Haploinsufficiency of the NF1 gene may protect against T2D and probably T1D. Since NF1 negatively regulates the Ras signalling pathway, the results suggest that the Ras pathway may be involved in the pathogenesis of diabetes.

Targeting Glycoproteins as a therapeutic strategy for diabetes mellitus and its complications

OBJECTIVES

Glycoproteins are organic compounds formed from proteins and carbohydrates, which are found in many parts of the living systems including the cell membranes. Furthermore, impaired metabolism of glycoprotein components plays the main role in the pathogenesis of diabetes mellitus. The aim of this study is to investigate the influence of glycoprotein levels in the treatment of diabetes mellitus.

METHODS

All relevant papers in the English language were compiled by searching electronic databases, including Scopus, PubMed and Cochrane library. The keywords of glycoprotein, diabetes mellitus, glycan, glycosylation, and inhibitor were searched until January 2019.

RESULTS

Glycoproteins are pivotal elements in the regulation of cell proliferation, growth, maturation and signaling pathways. Moreover, they are involved in drug binding, drug transportation, efflux of chemicals and stability of therapeutic proteins. These functions, structure, composition, linkages, biosynthesis, significance and biological effects are discussed as related to their use as a therapeutic strategy for the treatment of diabetes mellitus and its complications.

CONCLUSIONS

The findings revealed several chemical and natural compounds have significant beneficial effects on glycoprotein metabolism. The comprehension of glycoprotein structure and functions are very essential and inevitable to enhance the knowledge of glycoengineering for glycoprotein-based therapeutics as may be required for the treatment of diabetes mellitus and its associated complications. Graphical abstract.

Association Between Severity of Diabetic Ketoacidosis at Diagnosis and Multiple Autoimmunity in Children With Type 1 Diabetes Mellitus: A Study From a Greek Tertiary Centre

OBJECTIVES

Type 1 diabetes mellitus is a chronic disorder associated with development of autoimmunity. In this work, we studied the relationship between severity of acidosis at diagnosis and future risk for autoimmunity development in children with type 1 diabetes.

METHODS

We investigated the presence of associated autoimmunity in 144 children with type 1 diabetes (mean ± standard deviation: age, 12.44±4.76 years; diabetes duration, 4.41±3.70 years). We identified the presence of thyroid disease, celiac disease, autoimmune gastritis and adrenal autoimmunity, and retrospectively reviewed the files for presence of diabetic ketoacidosis at diagnosis.

RESULTS

Autoimmunity prevalence was 16.7% for thyroid autoimmunity, 9.5% for celiac disease, 5% for gastric autoimmunity and 8.0% for multiple autoimmunities. There were strong associations between severe acidosis at diabetes diagnosis (pH<7.10) and development of thyroid autoimmunity (odds ratio [OR], 5.34; 95% confidence interval [CI], 1.90‒15.1; p<0.001), celiac disease (OR, 5.83; 95% CI, 1.19‒28.6; p=0.013), gastric autoimmunity (OR, 13.1; 95% CI, 1.22‒140; p=0.006) and multiple autoimmunity (OR, 26.7; 95% CI, 2.36‒301; p<0.01). The associations persisted after adjustment for sex, age at diabetes diagnosis, age at assessment, time since diabetes diagnosis and antiglutamic acid decarboxylase autoantibody status.

CONCLUSIONS

The severity of acidosis at diagnosis is strongly associated with the development of associated autoimmune diseases in children with type 1 diabetes and could act as a predictive factor for multiple autoimmunity development. This association can be either due to effect of acidosis on immune system or to the presence of a more aggressive diabetes endotype.