Cord blood insulinoma-associated protein 2 autoantibodies are associated with increased risk of type 1 diabetes in the population-based Diabetes Prediction in Skane study

Aims/hypothesis The aim of this study was to examine the effect of cord blood autoantibodies on the risk for type 1 diabetes in children followed prospectively from birth. Methods The Diabetes Prediction in Skane (DiPiS) study consists of 35,853 children from the general population born during 2000-2004. Samples were collected at birth and analysed for HLA genotypes and autoantibodies to glutamate decarboxylase 65 (GAD65), insulin and insulinoma-associated protein 2 (IA-2). After adjusting for HLA, sex, maternal age and parental type 1 diabetes, independent associations with risk of diabetes were assessed using multivariate Cox proportional hazards models. Results In total, 151 children (0.4%) had developed type 1 diabetes by the end of 2013 at a median age of 5.8 years (0.8-12.2 years). In the multivariate analysis, the presence of IA-2 autoantibodies (IA-2A) in cord blood (HR 6.88, 95% CI 1.46,32.4; p = 0.003), but not maternal diabetes (HR 1.38, 95% CI 0.24,7.84; p = 0.71), was associated with risk of developing type 1 diabetes. No increased risk could be seen for the presence of autoantibodies to GAD65 or insulin. Conclusions/interpretation Our study indicates that the presence of cord blood IA-2A superimposes maternal diabetes and other cord blood islet autoantibodies as a predictor of type 1 diabetes development in the child. These findings may be of significance for future screening and study protocols on type 1 diabetes prediction

Controversy in statistical analysis of functional magnetic resonance imaging data

To test the validity of statistical methods for fMRI data analysis, Eklund et al. (1) used, for the first time, large-scale experimental data rather than simulated data. Using resting-state fMRI measurements to represent a null hypothesis of no task-induced activation, the authors compare familywise error rates for voxel-based and cluster-based inferences for both parametric and nonparametric methods. Eklund et al.’s study used three fMRI statistical analysis packages. They found that, for a target familywise error rate of 5%, the parametric methods gave invalid cluster-based inferences and conservative voxel-based inferences

Early-childhood body mass index and its association with the COVID-19 pandemic, containment measures and islet autoimmunity in children with increased risk for type 1 diabetes

Aims/hypothesis: The aim of this study was to determine whether BMI in early childhood was affected by the COVID-19 pandemic and containment measures, and whether it was associated with the risk for islet autoimmunity. Methods: Between February 2018 and May 2023, data on BMI and islet autoimmunity were collected from 1050 children enrolled in the Primary Oral Insulin Trial, aged from 4.0 months to 5.5 years of age. The start of the COVID-19 pandemic was defined as 18 March 2020, and a stringency index was used to assess the stringency of containment measures. Islet autoimmunity was defined as either the development of persistent confirmed multiple islet autoantibodies, or the development of one or more islet autoantibodies and type 1 diabetes. Multivariate linear mixed-effect, linear and logistic regression methods were applied to assess the effect of the COVID-19 pandemic and the stringency index on early-childhood BMI measurements (BMI as a time-varying variable, BMI at 9 months of age and overweight risk at 9 months of age), and Cox proportional hazard models were used to assess the effect of BMI measurements on islet autoimmunity risk. Results: The COVID-19 pandemic was associated with increased time-varying BMI (β = 0.39; 95% CI 0.30, 0.47) and overweight risk at 9 months (β = 0.44; 95% CI 0.03, 0.84). During the COVID-19 pandemic, a higher stringency index was positively associated with time-varying BMI (β = 0.02; 95% CI 0.00, 0.04 per 10 units increase), BMI at 9 months (β = 0.13; 95% CI 0.01, 0.25) and overweight risk at 9 months (β = 0.23; 95% CI 0.03, 0.43). A higher age-corrected BMI and overweight risk at 9 months were associated with increased risk for developing islet autoimmunity up to 5.5 years of age (HR 1.16; 95% CI 1.01, 1.32 and HR 1.68, 95% CI 1.00, 2.82, respectively). Conclusions/interpretation: Early-childhood BMI increased during the COVID-19 pandemic, and was influenced by the level of restrictions during the pandemic. Controlling for the COVID-19 pandemic, elevated BMI during early childhood was associated with increased risk for childhood islet autoimmunity in children with genetic susceptibility to type 1 diabetes. Graphical Abstract

Factors Associated with Decline of C-peptide in a Cohort of Young Children Diagnosed with Type 1 Diabetes

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

Beta cell function in participants with single or multiple islet autoantibodies at baseline in the TEDDY Family Prevention Study: TEFA

AimThe aim of the present study was to assess beta cell function based on an oral glucose tolerance test (OGTT) in participants with single islet autoantibody or an intravenous glucose tolerance test (IvGTT) in participants with multiple islet autoantibodies.Materials and methodsHealthy participants in Sweden and Finland, between 2 and 49.99 years of age previously identified as positive for a single (n = 30) autoantibody to either insulin, glutamic acid decarboxylase, islet antigen-2, zinc transporter 8 or islet cell antibodies or multiple autoantibodies (n = 46), were included. Participants positive for a single autoantibody underwent a 6-point OGTT while participants positive for multiple autoantibodies underwent an IvGTT. Glucose, insulin and C-peptide were measured from OGTT and IvGTT samples.ResultsAll participants positive for a single autoantibody had a normal glucose tolerance test with 120 minutes glucose below 7.70 mmol/L and HbA1c values within the normal range (ConclusionParticipants positive for a single autoantibody appeared to have a normal beta cell function. Participants positive for three or more autoantibodies had a lower FPIR as compared to participants with two autoantibodies, supporting the view that their beta cell function had deteriorated.</p

A method for reporting and classifying acute infectious diseases in a prospective study of young children : TEDDY

M. Knip työryhmän TEDDY Study Grp jäsen.Background: Early childhood environmental exposures, possibly infections, may be responsible for triggering islet autoimmunity and progression to type 1 diabetes (T1D). The Environmental Determinants of Diabetes in the Young (TEDDY) follows children with increased HLA-related genetic risk for future T1D. TEDDY asks parents to prospectively record the child's infections using a diary book. The present paper shows how these large amounts of partially structured data were reduced into quantitative data-sets and further categorized into system-specific infectious disease episodes. The numbers and frequencies of acute infections and infectious episodes are shown. Methods: Study subjects (n = 3463) included children who had attended study visits every three months from age 3 months to 4 years, without missing two or more consecutive visits during the follow-up. Parents recorded illnesses prospectively in a TEDDY Book at home. The data were entered into the study database during study visits using ICD-10 codes by a research nurse. TEDDY investigators grouped ICD-10 codes and fever reports into infectious disease entities and further arranged them into four main categories of infectious episodes: respiratory, gastrointestinal, other, and unknown febrile episodes. Incidence rate of infections was modeled as function of gender, HLA-DQ genetic risk group and study center using the Poisson regression. Results: A total of 113,884 ICD-10 code reports for infectious diseases recorded in the database were reduced to 71,578 infectious episodes, including 74.0% respiratory, 13.1% gastrointestinal, 5.7% other infectious episodes and 7.2% febrile episodes. Respiratory and gastrointestinal infectious episodes were more frequent during winter. Infectious episode rates peaked at 6 months and began declining after 18 months of age. The overall infectious episode rate was 5.2 episodes per person-year and varied significantly by country of residence, sex and HLA genotype. Conclusions: The data reduction and categorization process developed by TEDDY enables analysis of single infectious agents as well as larger arrays of infectious agents or clinical disease entities. The preliminary descriptive analyses of the incidence of infections among TEDDY participants younger than 4 years fits well with general knowledge of infectious disease epidemiology. This protocol can be used as a template in forthcoming time-dependent TEDDY analyses and in other epidemiological studies.Peer reviewe

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 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.</p

Decline in Titers of Anti-Idiotypic Antibodies Specific to Autoantibodies to GAD65 (GAD65Ab) Precedes Development of GAD65Ab and Type 1 Diabetes.

The humoral Idiotypic Network consisting of antibodies and their anti-idiotypic antibodies (anti-Id) can be temporarily upset by antigen exposure. In the healthy immune response the original equilibrium is eventually restored through counter-regulatory mechanisms. In certain autoimmune diseases however, autoantibody levels exceed those of their respective anti-Id, indicating a permanent disturbance in the respective humoral Idiotypic Network. We investigated anti-Id directed to a major Type 1 diabetes (T1D)-associated autoantibody (GAD65Ab) in two independent cohorts during progression to disease. Samples taken from participants of the Natural History Study showed significantly lower anti-Id levels in individuals that later progressed to T1D compared to non-progressors (anti-Id antibody index of 0.06 vs. 0.08, respectively, p = 0.02). We also observed a significant inverse correlation between anti-Id levels and age at sampling, but only in progressors (p = 0.014). Finally, anti-Id levels in progressors showed a significant decline during progression as compared to longitudinal anti-Id levels in non-progressors (median rate of change: -0.0004 vs. +0.0004, respectively, p = 0.003), suggesting a loss of anti-Id during progression. Our analysis of the Diabetes Prediction in Skåne cohort showed that early in life (age 2) individuals at risk have anti-Id levels indistinguishable from those in healthy controls, indicating that low anti-Id levels are not an innate characteristic of the immune response in individuals at risk. Notably, anti-Id levels declined significantly in individuals that later developed GAD65Ab suggesting that the decline in anti-Id levels precedes the emergence of GAD65Ab (median rate of change: -0.005) compared to matched controls (median rate of change: +0.001) (p = 0.0016). We conclude that while anti-Id are present early in life, their levels decrease prior to the appearance of GAD65Ab and to the development of T1D

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic. Graphical Abstract: (Figure presented.)Peer reviewe

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.Peer reviewe