Anxiety and Risk Perception in Parents of Children Identified by Population Screening as High Risk for Type 1 Diabetes

OBJECTIVE

To assess anxiety and risk perception among parents whose children screened positive for islet autoantibodies, indicating elevated risk for type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

The Autoimmunity Screening for Kids (ASK) study identified 319 children age 1 to 17 years at risk for T1D via screening for islet autoantibodies; 280 children with confirmed islet autoantibodies and their caregivers enrolled in a follow-up education and monitoring program to prevent diabetic ketoacidosis at diagnosis. Parents completed questionnaires at each monitoring visit, including a 6-item version of the State Anxiety Inventory (SAI), to assess anxiety about their child developing T1D, and a single question to assess risk perception.

RESULTS

At the first ASK follow-up monitoring visit, mean parental anxiety was elevated above the clinical cutoff of 40 (SAI 46.1 ± 11.2). At the second follow-up monitoring visit (i.e., visit 2), mean anxiety remained elevated but started to trend down. Approximately half (48.9%) of parents reported their child was at increased risk for T1D at the initial follow-up monitoring visit (visit 1). Parents of children with more than one islet autoantibody and a first-degree relative with T1D were more likely to report their child was at increased risk.

CONCLUSIONS

Most parents of autoantibody-positive children have high anxiety about their child developing T1D. Information about the risk of developing T1D is difficult to convey, as evidenced by the wide range of risk perception reported in this sample.

Refining the Definition of Stage 1 Type 1 Diabetes: An Ontology-Driven Analysis of the Heterogeneity of Multiple Islet Autoimmunity

OBJECTIVE

To estimate the risk of progression to stage 3 type 1 diabetes based on varying definitions of multiple islet autoantibody positivity (mIA).

RESEARCH DESIGN AND METHODS

Type 1 Diabetes Intelligence (T1DI) is a combined prospective data set of children from Finland, Germany, Sweden, and the U.S. who have an increased genetic risk for type 1 diabetes. Analysis included 16,709 infants-toddlers enrolled by age 2.5 years and comparison between groups using Kaplan-Meier survival analysis.

RESULTS

Of 865 (5%) children with mIA, 537 (62%) progressed to type 1 diabetes. The 15-year cumulative incidence of diabetes varied from the most stringent definition (mIA/Persistent/2: two or more islet autoantibodies positive at the same visit with two or more antibodies persistent at next visit; 88% [95% CI 85-92%]) to the least stringent (mIA/Any: positivity for two islet autoantibodies without co-occurring positivity or persistence; 18% [5-40%]). Progression in mIA/Persistent/2 was significantly higher than all other groups (P < 0.0001). Intermediate stringency definitions showed intermediate risk and were significantly different than mIA/Any (P < 0.05); however, differences waned over the 2-year follow-up among those who did not subsequently reach higher stringency. Among mIA/Persistent/2 individuals with three autoantibodies, loss of one autoantibody by the 2-year follow-up was associated with accelerated progression. Age was significantly associated with time from seroconversion to mIA/Persistent/2 status and mIA to stage 3 type 1 diabetes.

CONCLUSIONS

The 15-year risk of progression to type 1 diabetes risk varies markedly from 18 to 88% based on the stringency of mIA definition. While initial categorization identifies highest-risk individuals, short-term follow-up over 2 years may help stratify evolving risk, especially for those with less stringent definitions of mIA.

Plasma protein biomarkers predict the development of persistent autoantibodies and type 1 diabetes 6 months prior to the onset of autoimmunity

Type 1 diabetes (T1D) results from autoimmune destruction of β cells. Insufficient availability of biomarkers represents a significant gap in understanding the disease cause and progression. We conduct blinded, two-phase case-control plasma proteomics on the TEDDY study to identify biomarkers predictive of T1D development. Untargeted proteomics of 2,252 samples from 184 individuals identify 376 regulated proteins, showing alteration of complement, inflammatory signaling, and metabolic proteins even prior to autoimmunity onset. Extracellular matrix and antigen presentation proteins are differentially regulated in individuals who progress to T1D vs. those that remain in autoimmunity. Targeted proteomics measurements of 167 proteins in 6,426 samples from 990 individuals validate 83 biomarkers. A machine learning analysis predicts if individuals would remain in autoimmunity or develop T1D 6 months before autoantibody appearance, with areas under receiver operating characteristic curves of 0.871 and 0.918, respectively. Our study identifies and validates biomarkers, highlighting pathways affected during T1D development.

Increased Technology Use Associated With Lower A1C in a Large Pediatric Clinical Population

OBJECTIVE

While continuous glucose monitors (CGMs), insulin pumps, and hybrid closed-loop (HCL) systems each improve glycemic control in type 1 diabetes, it is unclear how the use of these technologies impacts real-world pediatric care.

RESEARCH DESIGN AND METHODS

We found 1,455 patients aged <22 years, with type 1 diabetes duration >3 months, and who had data from a single center in between both 2016-2017 (n = 2,827) and 2020-2021 (n = 2,731). Patients were grouped by multiple daily injections or insulin pump, with or without an HCL system, and using a blood glucose monitor or CGM. Glycemic control was compared using linear mixed-effects models adjusting for age, diabetes duration, and race/ethnicity.

RESULTS

CGM use increased from 32.9 to 75.3%, and HCL use increased from 0.3 to 27.9%. Overall A1C decreased from 8.9 to 8.6% (P < 0.0001).

CONCLUSIONS

Adoption of CGM and HCL was associated with decreased A1C, suggesting promotion of these technologies may yield glycemic benefits.

Genome-wide analysis of oxylipins and oxylipin profiles in a pediatric population

Background

Oxylipins are inflammatory biomarkers derived from omega-3 and-6 fatty acids implicated in inflammatory diseases but have not been studied in a genome-wide association study (GWAS). The aim of this study was to identify genetic loci associated with oxylipins and oxylipin profiles to identify biologic pathways and therapeutic targets for oxylipins.

Methods

We conducted a GWAS of plasma oxylipins in 316 participants in the Diabetes Autoimmunity Study in the Young (DAISY). DNA samples were genotyped using the TEDDY-T1D Exome array, and additional variants were imputed using the Trans-Omics for Precision Medicine (TOPMed) multi-ancestry reference panel. Principal components analysis of 36 plasma oxylipins was used to capture oxylipin profiles. PC1 represented linoleic acid (LA)- and alpha-linolenic acid (ALA)-related oxylipins, and PC2 represented arachidonic acid (ARA)-related oxylipins. Oxylipin PC1, PC2, and the top five loading oxylipins from each PC were used as outcomes in the GWAS (genome-wide significance: p < 5×10-8).

Results

The SNP rs143070873 was associated with (p < 5×10-8) the LA-related oxylipin 9-HODE, and rs6444933 (downstream of CLDN11) was associated with the LA-related oxylipin 13 S-HODE. A locus between MIR1302-7 and LOC100131146, rs10118380 and an intronic variant in TRPM3 were associated with the ARA-related oxylipin 11-HETE. These loci are involved in inflammatory signaling cascades and interact with PLA2, an initial step to oxylipin biosynthesis.

Conclusion

Genetic loci involved in inflammation and oxylipin metabolism are associated with oxylipin levels.

Consortium-based approach to receiving an EMA qualification opinion on the use of islet autoantibodies as enrichment biomarkers in type 1 diabetes clinical studies

The development of medical products that can delay or prevent progression to stage 3 type 1 diabetes faces many challenges. Of note, optimising patient selection for type 1 diabetes prevention clinical trials is hindered by significant patient heterogeneity and a lack of characterisation of the time-varying probability of progression to stage 3 type 1 diabetes in individuals positive for two or more islet autoantibodies. To meet these needs, the Critical Path Institute's Type 1 Diabetes Consortium was launched in 2017 as a pre-competitive public-private partnership between stakeholders from the pharmaceutical industry, patient advocacy groups, philanthropic organisations, clinical researchers, the National Institutes of Health and the Food and Drug Administration. The Type 1 Diabetes Consortium acquired and aggregated data from three longitudinal observational studies, Environmental Determinants of Diabetes in the Young (TEDDY), Diabetes Autoimmunity Study in the Young (DAISY) and TrialNet Pathway to Prevention (TN01), and used analysis subsets of these data to support the model-based qualification of islet autoantibodies as enrichment biomarkers for patient selection in type 1 diabetes prevention trials, including registration studies. The Type 1 Diabetes Consortium has now received a qualification opinion from the European Medicines Agency for the use of these biomarkers, a major success for the field of type 1 diabetes. This endorsement will improve product developers' ability to design clinical trials of agents intended to prevent or delay type 1 diabetes that are reduced in size and/or length, while being adequately powered.

An Oxylipin-Related Nutrient Pattern and Risk of Type 1 Diabetes in the Diabetes Autoimmunity Study in the Young (DAISY)

Oxylipins, pro-inflammatory and pro-resolving lipid mediators, are associated with the risk of type 1 diabetes (T1D) and may be influenced by diet. This study aimed to develop a nutrient pattern related to oxylipin profiles and test their associations with the risk of T1D among youth. The nutrient patterns were developed with a reduced rank regression in a nested case-control study (n = 335) within the Diabetes Autoimmunity Study in the Young (DAISY), a longitudinal cohort of children at risk of T1D. The oxylipin profiles (adjusted for genetic predictors) were the response variables. The nutrient patterns were tested in the case-control study (n = 69 T1D cases, 69 controls), then validated in the DAISY cohort using a joint Cox proportional hazards model (n = 1933, including 81 T1D cases). The first nutrient pattern (NP1) was characterized by low beta cryptoxanthin, flavanone, vitamin C, total sugars and iron, and high lycopene, anthocyanidins, linoleic acid and sodium. After adjusting for T1D family history, the HLA genotype, sex and race/ethnicity, NP1 was associated with a lower risk of T1D in the nested case-control study (OR: 0.44, p = 0.0126). NP1 was not associated with the risk of T1D (HR: 0.54, p-value = 0.1829) in the full DAISY cohort. Future studies are needed to confirm the nested case-control findings and investigate the modifiable factors for oxylipins.

Quantifying the utility of islet autoantibody levels in the prediction of type 1 diabetes in children

AIMS/HYPOTHESIS

The aim of this study was to explore the utility of islet autoantibody (IAb) levels for the prediction of type 1 diabetes in autoantibody-positive children.

METHODS

Prospective cohort studies in Finland, Germany, Sweden and the USA followed 24,662 children at increased genetic or familial risk of developing islet autoimmunity and diabetes. For the 1403 who developed IAbs (523 of whom developed diabetes), levels of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA) and insulinoma-associated antigen-2 (IA-2A) were harmonised for analysis. Diabetes prediction models using multivariate logistic regression with inverse probability censored weighting (IPCW) were trained using 10-fold cross-validation. Discriminative power for disease was estimated using the IPCW concordance index (C index) with 95% CI estimated via bootstrap.

RESULTS

A baseline model with covariates for data source, sex, diabetes family history, HLA risk group and age at seroconversion with a 10-year follow-up period yielded a C index of 0.61 (95% CI 0.58, 0.63). The performance improved after adding the IAb positivity status for IAA, GADA and IA-2A at seroconversion: C index 0.72 (95% CI 0.71, 0.74). Using the IAb levels instead of positivity indicators resulted in even better performance: C index 0.76 (95% CI 0.74, 0.77). The predictive power was maintained when using the IAb levels alone: C index 0.76 (95% CI 0.75, 0.76). The prediction was better for shorter follow-up periods, with a C index of 0.82 (95% CI 0.81, 0.83) at 2 years, and remained reasonable for longer follow-up periods, with a C index of 0.76 (95% CI 0.75, 0.76) at 11 years. Inclusion of the results of a third IAb test added to the predictive power, and a suitable interval between seroconversion and the third test was approximately 1.5 years, with a C index of 0.78 (95% CI 0.77, 0.78) at 10 years follow-up.

CONCLUSIONS/INTERPRETATION

Consideration of quantitative patterns of IAb levels improved the predictive power for type 1 diabetes in IAb-positive children beyond qualitative IAb positivity status.

Prevalence of SARS-CoV-2 Antibodies Among Healthy Children From Colorado From 2020 to 2021: A Brief Report

There are few estimates of the seroprevalence of SARS-CoV-2 antibodies among children in the United States. We measured vaccine and infection induced seroprevalence among nearly 5000 healthy 1 to 17-year-old children in Colorado from 2020 to 2021. By December 2021, 89% of older children, ages 12 to 18, had antibodies detected. The increase was largely driven from vaccination rather than infection.

Islet Autoantibody Levels Differentiate Progression Trajectories in Individuals With Presymptomatic Type 1 Diabetes

In our previous data-driven analysis of evolving patterns of islet autoantibodies (IAb) against insulin (IAA), GAD (GADA), and islet antigen 2 (IA-2A), we discovered three trajectories, characterized according to multiple IAb (TR1), IAA (TR2), or GADA (TR3) as the first appearing autoantibodies. Here we examined the evolution of IAb levels within these trajectories in 2,145 IAb-positive participants followed from early life and compared those who progressed to type 1 diabetes (n = 643) with those remaining undiagnosed (n = 1,502). With use of thresholds determined by 5-year diabetes risk, four levels were defined for each IAb and overlaid onto each visit. In diagnosed participants, high IAA levels were seen in TR1 and TR2 at ages <3 years, whereas IAA remained at lower levels in the undiagnosed. Proportions of dwell times (total duration of follow-up at a given level) at the four IAb levels differed between the diagnosed and undiagnosed for GADA and IA-2A in all three trajectories (P < 0.001), but for IAA dwell times differed only within TR2 (P < 0.05). Overall, undiagnosed participants more frequently had low IAb levels and later appearance of IAb than diagnosed participants. In conclusion, while it has long been appreciated that the number of autoantibodies is an important predictor of type 1 diabetes, consideration of autoantibody levels within the three autoimmune trajectories improved differentiation of IAb-positive children who progressed to type 1 diabetes from those who did not.

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.

Imputing Longitudinal Growth Data in International Pediatric Studies: Does CDC Reference Suffice?

This study investigates a missing value imputation approach for longitudinal growth data in pediatric studies from multiple countries. We analyzed a combined cohort from five natural history studies of type 1 diabetes (T1D) in the US and EU with longitudinal growth measurements for 23,201 subjects. We developed a multiple imputation methodology using LMS parameters of CDC reference data. We measured imputation errors on both combined and individual cohorts using mean absolute percentage error (MAPE) and normalized root-mean-square error (NRMSE). Our results show low imputation errors using CDC reference. Overall height imputation errors were lower than for weight. The largest MAPE for weight and height among all age groups was 4.8% and 1.7%, respectively. When comparing performance between CDC reference and country-specific growth charts, we found no significant differences for height (CDC vs. German: p =0.993, CDC vs. Swedish: p=0.368) and for weight (CDC vs. Swedish: p=0.513) for all ages.

CGM Metrics Predict Imminent Progression to Type 1 Diabetes: Autoimmunity Screening for Kids (ASK) Study

OBJECTIVE

Children identified with stage 1 type 1 diabetes are at high risk for progressing to stage 3 (clinical) diabetes and require accurate monitoring. Our aim was to establish continuous glucose monitoring (CGM) metrics that could predict imminent progression to diabetes.

RESEARCH DESIGN AND METHODS

In the Autoimmunity Screening for Kids study, 91 children who were persistently islet autoantibody positive (median age 11.5 years; 48% non-Hispanic White; 57% female) with a baseline CGM were followed for development of diabetes for a median of 6 (range 0.2-34) months. Of these, 16 (18%) progressed to clinical diabetes in a median of 4.5 (range 0.4-29) months.

RESULTS

Compared with children who did not progress to clinical diabetes (nonprogressors), those who did (progressors) had significantly higher average sensor glucose levels (119 vs. 105 mg/dL, P < 0.001) and increased glycemic variability (SD 27 vs. 16, coefficient of variation, 21 vs. 15, mean of daily differences 24 vs. 16, and mean amplitude of glycemic excursions 43 vs. 26, all P < 0.001). For progressors, 21% of the time was spent with glucose levels >140 mg/dL (TA140) and 8% of time >160 mg/dL, compared with 3% and 1%, respectively, for nonprogressors. In survival analyses, the risk of progression to diabetes in 1 year was 80% in those with TA140 >10%; in contrast, it was only 5% in the other participants. Performance of prediction by receiver operating curve analyses showed area under the curve of ≥0.89 for both individual and combined CGM metric models.

CONCLUSIONS

TA140 >10% is associated with a high risk of progression to clinical diabetes within the next year in autoantibody-positive children. CGM should be included in the ongoing monitoring of high-risk children and could be used as potential entry criterion for prevention trials.

Islet Autoantibody Type-Specific Titer Thresholds Improve Stratification of Risk of Progression to Type 1 Diabetes in Children

OBJECTIVE

To use islet autoantibody titers to improve the estimation of future type 1 diabetes risk in children.

RESEARCH DESIGN AND METHODS

Prospective cohort studies in Finland, Germany, Sweden, and the U.S. followed 24,662 children at increased genetic or familial risk to develop islet autoimmunity and diabetes. For 1,604 children with confirmed positivity, titers of autoantibodies against insulin (IAA), GAD antibodies (GADA), and insulinoma-associated antigen 2 (IA-2A) were harmonized for diabetes risk analyses.

RESULTS

Survival analysis from time of confirmed positivity revealed markedly different 5-year diabetes risks associated with IAA (n = 909), GADA (n = 1,076), and IA-2A (n = 714), when stratified by quartiles of titer, ranging from 19% (GADA 1st quartile) to 60% (IA-2A 4th quartile). The minimum titer associated with a maximum difference in 5-year risk differed for each autoantibody, corresponding to the 58.6th, 52.4th, and 10.2nd percentile of children specifically positive for each of IAA, GADA, and IA-2A, respectively. Using these autoantibody type-specific titer thresholds in the 1,481 children with all autoantibodies tested, the 5-year risk conferred by single (n = 954) and multiple (n = 527) autoantibodies could be stratified from 6 to 75% (P < 0.0001). The thresholds effectively identified children with a ≥50% 5-year risk when considering age-specific autoantibody screening (57-65% positive predictive value and 56-74% sensitivity for ages 1-5 years). Multivariable analysis confirmed the significance of associations between the three autoantibody titers and diabetes risk, informing a childhood risk surveillance strategy.

CONCLUSIONS

This study defined islet autoantibody type-specific titer thresholds that significantly improved type 1 diabetes risk stratification in children.

DPVis: Visual Analytics With Hidden Markov Models for Disease Progression Pathways

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

The oxylipin profile is associated with development of type 1 diabetes: the Diabetes Autoimmunity Study in the Young (DAISY)

AIMS/HYPOTHESIS

Oxylipins are lipid mediators derived from polyunsaturated fatty acids. Some oxylipins are proinflammatory (e.g. those derived from arachidonic acid [ARA]), others are pro-resolving of inflammation (e.g. those derived from α-linolenic acid [ALA], docosahexaenoic acid [DHA] and eicosapentaenoic acid [EPA]) and others may be both (e.g. those derived from linoleic acid [LA]). The goal of this study was to examine whether oxylipins are associated with incident type 1 diabetes.

METHODS

We conducted a nested case-control analysis in the Diabetes Autoimmunity Study in the Young (DAISY), a prospective cohort study of children at risk of type 1 diabetes. Plasma levels of 14 ARA-derived oxylipins, ten LA-derived oxylipins, six ALA-derived oxylipins, four DHA-derived oxylipins and two EPA-related oxylipins were measured by ultra-HPLC-MS/MS at multiple timepoints related to autoantibody seroconversion in 72 type 1 diabetes cases and 71 control participants, which were frequency matched on age at autoantibody seroconversion (of the case), ethnicity and sample availability. Linear mixed models were used to obtain an age-adjusted mean of each oxylipin prior to type 1 diabetes. Age-adjusted mean oxylipins were tested for association with type 1 diabetes using logistic regression, adjusting for the high risk HLA genotype HLA-DR3/4,DQB1*0302. We also performed principal component analysis of the oxylipins and tested principal components (PCs) for association with type 1 diabetes. Finally, to investigate potential critical timepoints, we examined the association of oxylipins measured before and after autoantibody seroconversion (of the cases) using PCs of the oxylipins at those visits.

RESULTS

The ARA-related oxylipin 5-HETE was associated with increased type 1 diabetes risk. Five LA-related oxylipins, two ALA-related oxylipins and one DHA-related oxylipin were associated with decreased type 1 diabetes risk. A profile of elevated LA- and ALA-related oxylipins (PC1) was associated with decreased type 1 diabetes risk (OR 0.61; 95% CI 0.40, 0.94). A profile of elevated ARA-related oxylipins (PC2) was associated with increased diabetes risk (OR 1.53; 95% CI 1.03, 2.29). A critical timepoint analysis showed type 1 diabetes was associated with a high ARA-related oxylipin profile at post-autoantibody-seroconversion but not pre-seroconversion.

CONCLUSIONS/INTERPRETATION

The protective association of higher LA- and ALA-related oxylipins demonstrates the importance of both inflammation promotion and resolution in type 1 diabetes. Proinflammatory ARA-related oxylipins may play an important role once the autoimmune process has begun.

Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

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.

Medical management of children with type 1 diabetes on low-carbohydrate or ketogenic diets

OBJECTIVES

Low-carbohydrate and ketogenic diets are becoming increasingly popular choices for people with type 1 diabetes (T1D) aiming to achieve optimal glycemic control. A carbohydrate-restricted diet in children has been associated with negative health effects including poor linear growth and inadequate bone mineralization. Guidelines for monitoring children and adolescents choosing to follow a carbohydrate-restricted diet do not exist. We aimed to create a clinical protocol outlining how to clinically and biochemically follow patients choosing a carbohydrate-restricted diet with the goal of medical safety.

METHODS

An interdisciplinary committee was formed and reviewed current consensus guidelines for pediatric patients on carbohydrate-restricted diets for epilepsy and metabolic disorders. A literature search was done to determine management strategies for children with T1D on a low-carbohydrate or ketogenic diet. Key health parameters that require monitoring were identified: growth, glycemic control, bone health, cardiometabolic health, and nutritional status. These health outcomes were used to develop a protocol for monitoring children on carbohydrate-restricted diets.

RESULTS

A one-page protocol for medical providers and educational materials for families interested in following a low-carbohydrate or ketogenic diet were developed and successfully implemented into clinical care.

CONCLUSION

Implementing a protocol for children on carbohydrate-restricted diets in clinic allows medical providers to ensure medical safety while being open to discussing a family's dietary preferences. Following children in the protocol over time will lead to informed clinical guidelines for patients with T1D who choose to follow a carbohydrate-restricted diet.

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.

Modeling Disease Progression Trajectories from Longitudinal Observational Data

Analyzing disease progression patterns can provide useful insights into the disease processes of many chronic conditions. These analyses may help inform recruitment for prevention trials or the development and personalization of treatments for those affected. We learn disease progression patterns using Hidden Markov Models (HMM) and distill them into distinct trajectories using visualization methods. We apply it to the domain of Type 1 Diabetes (T1D) using large longitudinal observational data from the T1DI study group. Our method discovers distinct disease progression trajectories that corroborate with recently published findings. In this paper, we describe the iterative process of developing the model. These methods may also be applied to other chronic conditions that evolve over time.

Gluten intake and risk of thyroid peroxidase autoantibodies in the Diabetes Autoimmunity Study In the Young (DAISY)

PURPOSE

Autoimmune diseases co-occur, perhaps due to common risk factors. The age at gluten introduction and gluten intake in early childhood has been associated with the autoimmunity preceding celiac disease (CD) and type-1 diabetes (T1D). We explored their associations with the development of thyroid autoimmunity.

METHODS

DAISY has prospectively followed children at increased risk for T1D and CD since 1993. During follow-up, 107 children developed thyroid autoimmunity, defined as positivity for autoantibodies against thyroid peroxidase on at least two study visits. Age at gluten introduction was ascertained from food history interviews every 3 months until 15 months of age. Gluten intake (g/day) at age 1-2 years was estimated using a food frequency questionnaire.

RESULTS

From multivariable Cox regression, there was no association between the age of gluten introduction nor the amount of gluten intake and development of thyroid autoimmunity. However, females (hazard ratio = 2.19, 95% CI: 1.46, 3.27) and cases of islet autoimmunity (HR = 2.20, 95% CI: 1.39, 3.50) were significantly more likely to develop thyroid autoimmunity, while exposure to environmental tobacco smoke decreased the risk (HR = 0.46, 95% CI: 0.30, 0.71).

CONCLUSIONS

Neither the age of gluten introduction nor the amount of gluten consumed in early childhood is associated with risk of thyroid autoimmunity.

Peer mentoring for professional and personal growth in academic medicine

Mentorship is a critical component of career development, particularly in academic medicine. Peer mentorship, which does not adhere to traditional hierarchies, is perhaps more accessible for underrepresented groups, including women and minorities. In this article, we review various models of peer mentorship, highlighting their respective advantages and disadvantages. Structured peer mentorship groups exist in different settings, such as those created under the auspices of formal career development programs, part of training grant programs, or through professional societies. Social media has further enabled the establishment of informal peer mentorship through participatory online groups, blogs, and forums that provide platforms for peer-to-peer advice and support. Such groups can evolve rapidly to address changing conditions, as demonstrated by physician listserv and Facebook groups related to the COVID-19 pandemic. Peer mentorship can also be found among colleagues brought together through a common location, interest, or goal, and typically these relationships are informal and fluid. Finally, we highlight here our experience with intentional formation of a small peer mentoring group that provides structure and a safe space for professional and social–emotional growth and support. In order to maximize impact and functionality, this model of peer mentorship requires commitment among peers and a more formalized process than many other peer mentoring models, accounting for group dynamics and the unique needs of members. When done successfully, the depth of these mentoring relationships can produce myriad benefits for individuals with careers in academic medicine including, but not limited to, those from underrepresented backgrounds.

Cost and Cost-effectiveness of Large-scale Screening for Type 1 Diabetes in Colorado

OBJECTIVE

To assess the costs and project the potential lifetime cost-effectiveness of the ongoing Autoimmunity Screening for Kids (ASK) program, a large-scale, presymptomatic type 1 diabetes screening program for children and adolescents in the metropolitan Denver region.

RESEARCH DESIGN AND METHODS

We report the resource utilization, costs, and effectiveness measures from the ongoing ASK program compared with usual care (i.e., no screening). Additionally, we report a practical screening scenario by including utilization and costs relevant to routine screening in clinical practice. Finally, we project the potential cost-effectiveness of ASK and routine screening by identifying clinical benchmarks (i.e., diabetic ketoacidosis [DKA] events avoided, HbA_1c improvements vs. no screening) needed to meet value thresholds of $50,000-$150,000 per quality-adjusted life-year (QALY) gained over a lifetime horizon.

RESULTS

Cost per case detected was $4,700 for ASK screening and $14,000 for routine screening. To achieve value thresholds of $50,000-$150,000 per QALY gained, screening costs would need to be offset by cost savings through 20% reductions in DKA events at diagnosis in addition to 0.1% (1.1 mmol/mol) improvements in HbA_1c over a lifetime compared with no screening for patients who develop type 1 diabetes. Value thresholds were not met from avoiding DKA events alone in either scenario.

CONCLUSIONS

Presymptomatic type 1 diabetes screening may be cost-effective in areas with a high prevalence of DKA and an infrastructure facilitating screening and monitoring if the benefits of avoiding DKA events and improved HbA_1c persist over long-run time horizons. As more data are collected from ASK, the model will be updated with direct evidence on screening effects.

DNA methylation near the INS gene is associated with INS genetic variation (rs689) and type 1 diabetes in the Diabetes Autoimmunity Study in the Young

OBJECTIVE

Mechanisms underlying the role of non-human leukocyte antigen (HLA) genetic risk variants in type 1 diabetes (T1D) are poorly understood. We aimed to test the association between methylation and non-HLA genetic risk.

METHODS

We conducted a methylation quantitative trait loci (mQTL) analysis in a nested case-control study from the Dietary Autoimmunity Study in the Young. Controls (n = 83) were frequency-matched to T1D cases (n = 83) based on age, race/ethnicity, and sample availability. We evaluated 13 non-HLA genetic markers known be associated with T1D. Genome-wide methylation profiling was performed on peripheral blood samples collected prior to T1D using the Illumina 450 K (discovery set) and infinium methylation EPIC beadchip (EPIC validation) platforms. Linear regression models, adjusting for age and sex, were used to test to each single nucleotide polymorphism (SNP) -probe combination. Logistic regression models were used to test the association between T1D and methylation levels among probes with a significant mQTL. A meta-analysis was used to combine odds ratios from the two platforms.

RESULTS

We identified 10 SNP-methylation probe pairs (false discovery rate (FDR) adjusted P < .05 and validation P < .05). Probes were associated with the GSDMB, C1QTNF6, IL27, and INS genes. The cg03366382 (OR: 1.9, meta-P = .0495), cg21574853 (OR: 2.5, meta-P = .0232), and cg25336198 (odds ratio: 6.6, meta-P = .0081) probes were significantly associated with T1D. The three probes were located upstream from the INS transcription start site.

CONCLUSIONS

We confirmed an association between DNA methylation and rs689 that has been identified in related studies. Measurements in our study preceded the onset of T1D suggesting methylation may have a role in the relationship between INS variation and T1D development.

Six months of hybrid closed loop in the real-world: An evaluation of children and young adults using the 670G system

OBJECTIVE

To describe glycemic and psychosocial outcomes in youth with type 1 diabetes using a hybrid closed loop (HCL) system.

SUBJECTS

Youth with type 1 diabetes (2-25 years) starting the 670G HCL system for their diabetes care were enrolled in an observational study.

METHODS

Prospective data collection occurred during routine clinical care and included glycemic variables (sensor time in range [70-180 mg/dL], HbA1c), and psychosocial variables (Hypoglycemia Fear Survey [HFS]; Problem Areas in Diabetes [PAID]). Mixed models were used to analyze change across time.

RESULTS

Ninety-two youth (mean age 15.7 ± 3.6 years, 50% female, HbA1c 8.8% ± 1.8%) started HCL for their diabetes care. Youth used Auto Mode 65.5% ± 3.0% of the time at month 1, which decreased to 51.2% ± 3.4% at month 6 (P = .001). Sensor time in range increased from 50.7% ± 1.8% at baseline to 56.9% ± 2.1% at 6 months (P = .007). HbA1c decreased from 8.7% ± 0.2% at baseline to 8.4% ± 0.2% after 6 months of use (P ≤ .0001), with the greatest HbA1c decline in participants with high baseline HbA1c. Increased percent time in auto mode was associated with lower HbA1c (P = .02). Thirty percent of youth discontinued HCL in the first 6 months of use. There were no changes in the HFS or PAID scores across time.

CONCLUSIONS

HCL use is associated with improved glycemic control and no change in psychosocial outcomes in this clinical sample. The decline in HCL use across time suggests that youth experience barriers in sustaining use of HCL. Further research is needed to understand reasons for HCL discontinuation and determine intervention strategies.

Bone Mineral Density across the Lifespan in Patients with Type 1 Diabetes

CONTEXT

Fracture risk in people with type 1 diabetes (T1D) is higher than their peers without diabetes.

OBJECTIVE

To compare bone mineral density (BMD) across the lifespan in individuals with T1D and age- and sex-matched healthy controls.

DESIGN

Cross-sectional.

SETTING

Subjects (5-71 years) with T1D and matched controls from ongoing research studies at Barbara Davis Center for Diabetes.

PATIENTS OR OTHER PARTICIPANTS

Participants with lumbar spine BMD by dual X-ray absorptiometry (DXA) were divided into 2 groups: children ≤20 years and adults >20 years.

INTERVENTION

None.

MAIN OUTCOME MEASURES

Comparison of BMD by diabetes status across age groups and sex using a linear least squares model adjusted for age and body mass index (body mass index (BMI) for adults; and BMI z-score in children).

RESULTS

Lumbar spine BMD from 194 patients with T1D and 156 controls were analyzed. There was no difference in age- and BMI-adjusted lumbar spine BMD between patients with T1D and controls: among male children (least squares mean ± standard error of the mean [LSM ± SEM]; 0.80 ± 0.01 vs 0.80 ± 0.02 g/cm2, P = .98) or adults (1.01 ± 0.03 vs 1.01 ± 0.03 g/cm2, P = .95), and female children (0.78 ± 0.02 vs 0.81 ± 0.02 g/cm2, P = .23) or adults (0.98 ± 0.02 vs 1.01 ± 0.02 g/cm2, P = .19). Lumbar spine (0.98 ± 0.02 vs 1.04 ± 0.02 g/cm2, P = .05), femoral neck (0.71 ± 0.02 vs 0.79 ± 0.02 g/cm2, P = .003), and total hip (0.84 ± 0.02 vs 0.91 ± 0.02, P = .005) BMD was lower among postmenopausal women with T1D than postmenopausal women without diabetes.

CONCLUSION

Across age groups, lumbar spine BMD was similar in patients with T1D compared with age- and sex-matched participants without diabetes, except postmenopausal females with T1D had lower lumbar spine, femoral neck, and total hip BMD.

Longitudinal DNA methylation differences precede type 1 diabetes

DNA methylation may be involved in development of type 1 diabetes (T1D), but previous epigenome-wide association studies were conducted among cases with clinically diagnosed diabetes. Using multiple pre-disease peripheral blood samples on the Illumina 450 K and EPIC platforms, we investigated longitudinal methylation differences between 87 T1D cases and 87 controls from the prospective Diabetes Autoimmunity Study in the Young (DAISY) cohort. Change in methylation with age differed between cases and controls in 10 regions. Average longitudinal methylation differed between cases and controls at two genomic positions and 28 regions. Some methylation differences were detectable and consistent as early as birth, including before and after the onset of preclinical islet autoimmunity. Results map to transcription factors, other protein coding genes, and non-coding regions of the genome with regulatory potential. The identification of methylation differences that predate islet autoimmunity and clinical diagnosis may suggest a role for epigenetics in T1D pathogenesis; however, functional validation is warranted.

Predictive Modeling of Type 1 Diabetes Stages Using Disparate Data Sources

This study aims to model genetic, immunologic, metabolomics, and proteomic biomarkers for development of islet autoimmunity (IA) and progression to type 1 diabetes in a prospective high-risk cohort. We studied 67 children: 42 who developed IA (20 of 42 progressed to diabetes) and 25 control subjects matched for sex and age. Biomarkers were assessed at four time points: earliest available sample, just prior to IA, just after IA, and just prior to diabetes onset. Predictors of IA and progression to diabetes were identified across disparate sources using an integrative machine learning algorithm and optimization-based feature selection. Our integrative approach was predictive of IA (area under the receiver operating characteristic curve [AUC] 0.91) and progression to diabetes (AUC 0.92) based on standard cross-validation (CV). Among the strongest predictors of IA were change in serum ascorbate, 3-methyl-oxobutyrate, and the PTPN22 (rs2476601) polymorphism. Serum glucose, ADP fibrinogen, and mannose were among the strongest predictors of progression to diabetes. This proof-of-principle analysis is the first study to integrate large, diverse biomarker data sets into a limited number of features, highlighting differences in pathways leading to IA from those predicting progression to diabetes. Integrated models, if validated in independent populations, could provide novel clues concerning the pathways leading to IA and type 1 diabetes.

Progression from islet autoimmunity to clinical type 1 diabetes is influenced by genetic factors: results from the prospective TEDDY study

BACKGROUND

Progression time from islet autoimmunity to clinical type 1 diabetes is highly variable and the extent that genetic factors contribute is unknown.

METHODS

In 341 islet autoantibody-positive children with the human leucocyte antigen (HLA) DR3/DR4-DQ8 or the HLA DR4-DQ8/DR4-DQ8 genotype from the prospective TEDDY (The Environmental Determinants of Diabetes in the Young) study, we investigated whether a genetic risk score that had previously been shown to predict islet autoimmunity is also associated with disease progression.

RESULTS

Islet autoantibody-positive children with a genetic risk score in the lowest quartile had a slower progression from single to multiple autoantibodies (p=0.018), from single autoantibodies to diabetes (p=0.004), and by trend from multiple islet autoantibodies to diabetes (p=0.06). In a Cox proportional hazards analysis, faster progression was associated with an increased genetic risk score independently of HLA genotype (HR for progression from multiple autoantibodies to type 1 diabetes, 1.27, 95% CI 1.02 to 1.58 per unit increase), an earlier age of islet autoantibody development (HR, 0.68, 95% CI 0.58 to 0.81 per year increase in age) and female sex (HR, 1.94, 95% CI 1.28 to 2.93).

CONCLUSIONS

Genetic risk scores may be used to identify islet autoantibody-positive children with high-risk HLA genotypes who have a slow rate of progression to subsequent stages of autoimmunity and type 1 diabetes.

Continuous Glucose Monitoring Predicts Progression to Diabetes in Autoantibody Positive Children

CONTEXT

Accurate measures are needed for the prediction and diagnosis of type 1 diabetes (T1D) in at-risk persons.

OBJECTIVE

The purpose of this study was to explore the value of continuous glucose monitoring (CGM) in predicting T1D onset.

DESIGN AND SETTING

The Diabetes Autoimmunity Study in the Young (DAISY) prospectively follows children at increased risk for development of islet autoantibodies (islet autoantibody positive; Ab+) and T1D.

PARTICIPANTS

We analyzed 23 Ab+ participants with available longitudinal CGM data.

MAIN OUTCOME MEASURE

CGM metrics as glycemic predictors of progression to T1D.

RESULTS

Of 23 Ab+ participants with a baseline CGM, 8 progressed to diabetes at a median age of 13.8 years during a median follow-up of 17.7 years (interquartile range, 14.6 to 22.0 years). Compared with nonprogressors, participants who progressed to diabetes had significantly increased baseline glycemic variability (SD, 29 vs 21 mg/dL; P = 0.047), daytime sensor average (122 vs 106 mg/dL; P = 0.02), and daytime sensor area under the curve (AUC, 470,370 vs 415,465; P = 0.047). They spent 24% of time at >140 mg/dL and 12% at >160 mg/dL compared with, respectively, 8% and 3% for nonprogressors (both P = 0.005). A receiver-operating characteristic curve analysis showed an AUC of 0.85 for percentage of time spent at >140 or 160 mg/dL. The cutoff of 18% time spent at >140 mg/dL had 75% sensitivity, 100% specificity, and a 100% positive predictive value for diabetes prediction, although these values could change because some nonprogressors may develop diabetes with longer follow-up.

CONCLUSIONS

Eighteen percent or greater CGM time spent at >140 mg/dL predicts progression to diabetes in Ab+ children.

Identification of non-HLA genes associated with development of islet autoimmunity and type 1 diabetes in the prospective TEDDY cohort

Traditional linkage analysis and genome-wide association studies have identified HLA and a number of non-HLA genes as genetic factors for islet autoimmunity (IA) and type 1 diabetes (T1D). However, the relative risk associated with previously identified non-HLA genes is usually very small as measured in cases/controls from mixed populations. Genetic associations for IA and T1D may be more accurately assessed in prospective cohorts. In this study, 5806 subjects from the TEDDY (The Environmental Determinants of Diabetes in the Young) study, an international prospective cohort study, were genotyped for 176,586 SNPs on the ImmunoChip. Cox proportional hazards analyses were performed to discover the SNPs associated with the risk for IA, T1D, or both. Three regions were associated with the risk of developing any persistent confirmed islet autoantibody: one known region near SH2B3 (HR = 1.35, p = 3.58 × 10-7) with Bonferroni-corrected significance and another known region near PTPN22 (HR = 1.46, p = 2.17 × 10-6) and one novel region near PPIL2 (HR = 2.47, p = 9.64 × 10-7) with suggestive evidence (p < 10-5). Two known regions (PTPN22: p = 2.25 × 10-6, INS; p = 1.32 × 10-7) and one novel region (PXK/PDHB: p = 8.99 × 10-6) were associated with the risk for multiple islet autoantibodies. First appearing islet autoantibodies differ with respect to association. Two regions (INS: p = 5.67 × 10-6 and TTC34/PRDM16: 6.45 × 10-6) were associated if the fist appearing autoantibody was IAA and one region (RBFOX1: p = 8.02 × 10-6) was associated if the first appearing autoantibody was GADA. The analysis of T1D identified one region already known to be associated with T1D (INS: p = 3.13 × 10-7) and three novel regions (RNASET2, PLEKHA1, and PPIL2; 5.42 × 10-6 > p > 2.31 × 10-6). These results suggest that a number of low frequency variants influence the risk of developing IA and/or T1D and these variants can be identified by large prospective cohort studies using a survival analysis approach.

Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children

BACKGROUND

Around 0.3% of newborns will develop autoimmunity to pancreatic beta cells in childhood and subsequently develop type 1 diabetes before adulthood. Primary prevention of type 1 diabetes will require early intervention in genetically at-risk infants. The objective of this study was to determine to what extent genetic scores (two previous genetic scores and a merged genetic score) can improve the prediction of type 1 diabetes.

METHODS AND FINDINGS

The Environmental Determinants of Diabetes in the Young (TEDDY) study followed genetically at-risk children at 3- to 6-monthly intervals from birth for the development of islet autoantibodies and type 1 diabetes. Infants were enrolled between 1 September 2004 and 28 February 2010 and monitored until 31 May 2016. The risk (positive predictive value) for developing multiple islet autoantibodies (pre-symptomatic type 1 diabetes) and type 1 diabetes was determined in 4,543 children who had no first-degree relatives with type 1 diabetes and either a heterozygous HLA DR3 and DR4-DQ8 risk genotype or a homozygous DR4-DQ8 genotype, and in 3,498 of these children in whom genetic scores were calculated from 41 single nucleotide polymorphisms. In the children with the HLA risk genotypes, risk for developing multiple islet autoantibodies was 5.8% (95% CI 5.0%-6.6%) by age 6 years, and risk for diabetes by age 10 years was 3.7% (95% CI 3.0%-4.4%). Risk for developing multiple islet autoantibodies was 11.0% (95% CI 8.7%-13.3%) in children with a merged genetic score of >14.4 (upper quartile; n = 907) compared to 4.1% (95% CI 3.3%-4.9%, P < 0.001) in children with a genetic score of ≤14.4 (n = 2,591). Risk for developing diabetes by age 10 years was 7.6% (95% CI 5.3%-9.9%) in children with a merged score of >14.4 compared with 2.7% (95% CI 1.9%-3.6%) in children with a score of ≤14.4 (P < 0.001). Of 173 children with multiple islet autoantibodies by age 6 years and 107 children with diabetes by age 10 years, 82 (sensitivity, 47.4%; 95% CI 40.1%-54.8%) and 52 (sensitivity, 48.6%, 95% CI 39.3%-60.0%), respectively, had a score >14.4. Scores were higher in European versus US children (P = 0.003). In children with a merged score of >14.4, risk for multiple islet autoantibodies was similar and consistently >10% in Europe and in the US; risk was greater in males than in females (P = 0.01). Limitations of the study include that the genetic scores were originally developed from case-control studies of clinical diabetes in individuals of mainly European decent. It is, therefore, possible that it may not be suitable to all populations.

CONCLUSIONS

A type 1 diabetes genetic score identified infants without family history of type 1 diabetes who had a greater than 10% risk for pre-symptomatic type 1 diabetes, and a nearly 2-fold higher risk than children identified by high-risk HLA genotypes alone. This finding extends the possibilities for enrolling children into type 1 diabetes primary prevention trials.

Prediction of type 1 diabetes using a genetic risk model in the Diabetes Autoimmunity Study in the Young

BACKGROUND

Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic regions confer susceptibility. We evaluated a previously reported 10-factor weighted model derived from the Type 1 Diabetes Genetics Consortium to predict the development of diabetes in the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. Performance of the model, derived from individuals with first-degree relatives (FDR) with T1D, was evaluated in DAISY general population (GP) participants as well as FDR subjects.

METHODS

The 10-factor weighted risk model (HLA, PTPN22 , INS , IL2RA , ERBB3 , ORMDL3 , BACH2 , IL27 , GLIS3 , RNLS ), 3-factor model (HLA, PTPN22, INS ), and HLA alone were compared for the prediction of diabetes in children with complete SNP data (n = 1941).

RESULTS

Stratification by risk score significantly predicted progression to diabetes by Kaplan-Meier analysis (GP: P = .00006; FDR: P = .0022). The 10-factor model performed better in discriminating diabetes outcome than HLA alone (GP, P = .03; FDR, P = .01). In GP, the restricted 3-factor model was superior to HLA (P = .03), but not different from the 10-factor model (P = .22). In contrast, for FDR the 3-factor model did not show improvement over HLA (P = .12) and performed worse than the 10-factor model (P = .02) CONCLUSIONS: We have shown a 10-factor risk model predicts development of diabetes in both GP and FDR children. While this model was superior to a minimal model in FDR, it did not confer improvement in GP. Differences in model performance in FDR vs GP children may lead to important insights into screening strategies specific to these groups.

Predicting progression to diabetes in islet autoantibody positive children

While full oral glucose tolerance test (OGTT) helps improve prediction, it requires intravenous access with 6 sample collections for glucose and C-peptide. The objective of this study was to explore less costly and less time-consuming options. All children being prospectively followed by the Diabetes Autoimmunity Study in the Young (DAISY) who had a complete baseline OGTT and at least one confirmed islet autoantibody (Ab+) were included in this study (n = 68). Of 68 Ab+ subjects with a baseline OGTT, 25 developed diabetes after a mean follow-up 5.7 yrs, at a mean age of 12.4 yrs. Univariate proportional hazards (PH) models suggested that age at seroconversion, number of Ab+, IA-2A levels, HbA1c and metabolic variables from the OGTT predicted progression to diabetes, while HLA DR3/4, BMI, levels of IAA or GADA did not. Five multivariate PH predictive models were similar (p = 0.32). All five models included age at seroconversion, number of Ab+, IA-2A levels and HbA1c, and in addition included: model 1 - 1 h glucose and 1 h C-peptide; model 2 - 2 h glucose and 2 h C-peptide; model 3 - glucose sum and C-peptide sum; model 4 - glucose AUC and C-peptide AUC; and model 5: index 60. A model containing age at seroconversion, number of Ab+, IA-2A levels, HbA1c, 1 h glucose and 1 h C-peptide was as predictive for type 1 diabetes progression as models including all sum or AUC values for glucose and C-peptide from full OGTT. The performance of this model should be confirmed in an independent population of Ab+ children.

Late-onset islet autoimmunity in childhood: the Diabetes Autoimmunity Study in the Young (DAISY)

AIMS/HYPOTHESIS

We sought to assess the frequency, determinants and prognosis for future diabetes in individuals with islet autoimmunity and whether these factors differ depending on the age of onset of islet autoimmunity.

METHODS

A prospective cohort (n = 2547) of children from the general population who had a high-risk HLA genotype and children who had a first-degree relative with type 1 diabetes were followed for up to 21 years. Those with the persistent presence of one or more islet autoantibodies were categorised as early-onset (<8 years of age, n = 143, median 3.3 years) or late-onset (≥8 years of age, n = 64, median 11.1 years), and were followed for a median of 7.4 and 4.7 years, respectively. Progression to diabetes was evaluated by Kaplan-Meier analysis with logrank test. Factors associated with progression to diabetes were analysed using the parametric accelerated failure time model.

RESULTS

Children with late-onset islet autoimmunity were more likely to be Hispanic or African-American than non-Hispanic white (p = 0.004), and less likely to be siblings of individuals with type 1 diabetes (p = 0.04). The frequencies of the HLA-DR3/4 genotype and non-HLA gene variants associated with type 1 diabetes did not differ between the two groups. However, age and HLA-DR3/4 were important predictors of rate of progression to both the presence of additional autoantibodies and type 1 diabetes. Late-onset islet autoimmunity was more likely to present with a single islet autoantibody (p = 0.01) and revert to an antibody-negative state (p = 0.01). Progression to diabetes was significantly slower in children with late-onset islet autoimmunity (p < 0.001).

CONCLUSIONS/INTERPRETATION

A late onset of islet autoimmunity is more common in African-American and Hispanic individuals. About half of those with late-onset islet autoimmunity progress to show multiple islet autoantibodies and develop diabetes in adolescence or early adulthood. Further investigation of environmental determinants of late-onset autoimmunity may lead to an understanding of and ability to prevent adolescent and adult-onset type 1 diabetes.

High Incidence of Celiac Disease in a Long-term Study of Adolescents With Susceptibility Genotypes

BACKGROUND & AIMS

Little is known about the incidence of celiac disease in the general population of children in the United States. We aimed to estimate the cumulative incidence of celiac disease in adolescents born in the Denver metropolitan area.

METHODS

We collected data on HLA-DR, DQ genotypes of 31,766 infants, born from 1993 through 2004 at St. Joseph's Hospital in Denver, from the Diabetes Autoimmunity Study in the Young. Subjects with susceptibility genotypes for celiac disease and type 1 diabetes were followed up for up to 20 years for development of tissue transglutaminase autoantibodies (tTGA). Outcomes were the development of celiac disease autoimmunity (CDA) or celiac disease. CDA was defined as persistence of tTGA for at least 3 months or development of celiac disease. Celiac disease was defined based on detection of Marsh 2 or greater lesions in biopsy specimens or persistent high levels of tTGA. For each genotype, the cumulative incidence of CDA and celiac disease were determined. To estimate the cumulative incidence in the Denver general population, outcomes by each genotype were weighted according to the frequency of each of these genotypes in the general population.

RESULTS

Of 1339 subjects followed up, 66 developed CDA and met criteria for celiac disease and 46 developed only CDA. Seropositivity for tTGA resolved spontaneously, without treatment, in 21 of the 46 subjects with only CDA (46%). The estimated cumulative incidence for CDA in the Denver general population at 5, 10, and 15 years of age was 2.4%, 4.3%, and 5.1%, respectively, and incidence values for celiac disease were 1.6%, 2.8%, and 3.1%, respectively.

CONCLUSIONS

In a 20-year prospective study of 1339 children with genetic risk factors for celiac disease, we found the cumulative incidence of CDA and celiac disease to be high within the first 10 years. Although more than 5% of children may experience a period of CDA, not all children develop celiac disease or require gluten-free diets.

Roux-en-Y Gastric Bypass Acutely Decreases Protein Carbonylation and Increases Expression of Mitochondrial Biogenesis Genes in Subcutaneous Adipose Tissue

BACKGROUND

Mitochondrial dysfunction in adipose tissue has been implicated as a pathogenic step in the development of type 2 diabetes mellitus (T2DM). In adipose tissue, chronic nutrient overload results in mitochondria driven increased reactive oxygen species (ROS) leading to carbonylation of proteins that impair mitochondrial function and downregulation of key genes linked to mitochondrial biogenesis. In patients with T2DM, Roux-en-Y gastric bypass (RYGB) surgery leads to improvements in glycemic profile prior to significant weight loss. Consequently, we hypothesized that improved glycemia early after RYGB would be paralleled by decreased protein carbonylation and increased expression of genes related to mitochondrial biogenesis in adipose tissue.

METHODS

To evaluate this hypothesis, 16 obese individuals were studied before and 7-8 days following RYGB and adjustable gastric banding (AGB). Subcutaneous adipose tissue was obtained pre- and post-bariatric surgery as well as from eight healthy, non-obese individual controls.

RESULTS

Prior to surgery, adipose tissue expression of PGC1α, NRF1, Cyt C, and eNOS (but not Tfam) showed significantly lower expression in the obese bariatric surgery group when compared to lean controls (p < 0.05). Following RYGB, but not after AGB, patients showed significant decrease in HOMA-IR, reduction in adipose protein carbonylation, and increased expression of genes linked to mitochondrial biogenesis.

CONCLUSIONS

These results suggest that rapid reduction in protein carbonylation and increased mitochondrial biogenesis may explain postoperative metabolic improvements following RYGB.

Predictors of slow progression to diabetes in children with multiple islet autoantibodies

Although most children with multiple islet autoantibodies develop type 1 diabetes, rate of progression is highly variable. The goal of this study was to explore potential factors involved in rate of progression to diabetes in children with multiple islet autoantibodies. The Diabetes Autoimmunity Study in the Young (DAISY) has followed 118 children with multiple islet autoantibodies for progression to diabetes. After excluding 27 children currently diabetes-free but followed for <10 years, the study population was grouped into: rapid progressors (N = 39) who developed diabetes in <5 years; moderate progressors (N = 25), diagnosed with diabetes within 5-10 years; and slow progressors (N = 27), diabetes-free for >10 years. Islet autoimmunity appeared at 4.0 ± 3.5, 3.2 ± 1.8 and 5.8 ± 3.1 years of age in rapid, moderate and slow progressors, respectively (p = 0.006). Insulin autoantibody levels were lower in slow progressors compared to moderate and rapid progressors. The groups did not differ by gender, ethnicity, family history, susceptibility HLA and non-HLA genes. The rate of development of individual islet autoantibodies including mIAA, GADA, IA-2A and ZnT8A were all slower in the slow versus moderate/rapid progressors. In multivariate analyses, older age at seroconversion and lower initial mIAA levels independently predicted slower progression to diabetes. Later onset of islet autoimmunity and lower autoantibody levels predicted slower progression to diabetes among children with multiple islet autoantibodies. These factors may need to be considered in the design of trials to prevent type 1 diabetes.

Reduced Bone Mineral Density Is Associated with Celiac Disease Autoimmunity in Children with Type 1 Diabetes

OBJECTIVE

To evaluate the association between bone mineral density (BMD), glycemic control (hemoglobin A1c [HbA1c]), and celiac autoimmunity in children with type 1 diabetes mellitus (T1D) and in an appropriate control population.

STUDY DESIGN

BMD was assessed cross-sectionally in 252 children with T1D (123 positive for anti-tissue transglutaminase antibody [tTGA] and 129 matched children who were negative for tTGA). In addition, BMD was assessed in 141 children without diabetes who carried T1D-associated HLD-DR, DQ genotypes (71 positive for tTGA and 70 negative).

RESULTS

Children with T1D who were positive for tTGA had significantly worse BMD L1-L4 z-score compared with children with T1D who were negative for tTGA (-0.45 ± 1.22 vs 0.09 ± 1.10, P = .0003). No differences in growth measures, urine N-telopeptides, 25-hydroxyvitamin D, ferritin, thyroid stimulating hormone, or HbA1c were found. However, both higher HbA1c (β = -1.25 ± 0.85, P = .0016) and tTGA (β = -0.13 ± 0.05, P = .0056) were significant and independent predictors of lower BMD in multivariate analyses. No differences in BMD or other variables measured were found between children without diabetes who were positive vs negative for tTGA.

CONCLUSIONS

The results suggest a synergistic effect of hyperglycemia and celiac autoimmunity on low BMD.

Metabolomics in childhood diabetes

Recent increases in the incidence of both type 1 (T1D) and type 2 diabetes (T2D) in children and adolescents point to the importance of environmental factors in the development of these diseases. Metabolomic analysis explores the integrated response of the organism to environmental changes. Metabolic profiling can identify biomarkers that are predictive of disease incidence and development, potentially providing insight into disease pathogenesis. This review provides an overview of the role of metabolomic analysis in diabetes research and summarizes recent research relating to the development of T1D and T2D in children.

Higher daily physical activity is associated with higher osteocalcin levels in adolescents

Background

Exercise stimulates bone remodeling and improves insulin sensitivity (Si), even without associated weight loss. Osteocalcin (OCN), a bone-derived protein, is associated with improved Si.

Purpose

We examined how daily physical activity is associated with OCN and Si.

Methods

Physical activity was measured through questionnaires completed in Minneapolis from 2010 to 2012. A physical activity score (PAQsum) was calculated to quantify physical activity (range 1–5). OCN and bone specific alkaline phosphatase (BAP) were measured by ELISA. Si was measured by the insulin modified frequently sampled IV glucose tolerance test.

Results

The mean PAQsum value was 2.4 ± 0.8 in 47 participants (12–17.9 years old). PAQsum was positively associated with OCN (p = 0.006). Participants with PAQsum <  2 had significantly lower OCN levels compared to participants with PAQsum >  2 (p < 0.02). Obesity did not modify the association between PAQsum and OCN. There was no statistically significant association between PAQsum and Si or between OCN and Si, even after adjustment for percent body fat.

Conclusions

OCN is higher in more physically active individuals. More research is needed to clarify the relationship between OCN, physical activity and Si.

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We examined the influence of physical activity on OCN.

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Moderate physical activity is associated with higher levels of OCN.

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Obesity did not impact the positive association between physical activity and OCN.

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OCN may play a role in the insulin sensitizing effects of physical activity.

Glutathionylated products of lipid peroxidation: A novel mechanism of adipocyte to macrophage signaling

Obesity-associated insulin resistance has long been linked to both increased adipocyte oxidative stress as well as the presence of inflammatory changes in adipose tissue, including the infiltration and activation of tissue-resident macrophages. In order to investigate the connections between obesity-associated oxidative stress in adipocytes and increased inflammation in adipose tissue associated with the development of insulin resistance, our laboratory recently demonstrated that adipocytes form glutathionylated products of oxidative stress including glutathionyl-4-hydroxy-2-nonenal (GS-HNE) and glutathionyl-1,4-dihydroxynonene (GS-DHN). The abundance of both GS-HNE and GS-DHN were increased in the visceral adipose tissue of ob/ob mice and diet-induced obese, insulin-resistant mice. Further, these products of lipid peroxidation were shown to induce inflammatory changes in macrophages. Finally, in a mouse model, overproduction of GS-HNE was associated with increased fasting glucose levels and moderately impaired glucose tolerance. Together, these findings suggest a novel mechanism by which obesity-induced oxidative stress in adipocytes may lead to activation of tissue-resident macrophages. As adipose tissue inflammation has been shown to play an important role in the development of insulin resistance, further study of this pathway may lead to potential interventions to attenuate the metabolic consequences of obesity.

Glutathionylated lipid aldehydes are products of adipocyte oxidative stress and activators of macrophage inflammation

Obesity-induced insulin resistance has been linked to adipose tissue lipid aldehyde production and protein carbonylation. Trans-4-hydroxy-2-nonenal (4-HNE) is the most abundant lipid aldehyde in murine adipose tissue and is metabolized by glutathione S-transferase A4 (GSTA4), producing glutathionyl-HNE (GS-HNE) and its metabolite glutathionyl-1,4-dihydroxynonene (GS-DHN). The objective of this study was to evaluate adipocyte production of GS-HNE and GS-DHN and their effect on macrophage inflammation. Compared with lean controls, GS-HNE and GS-DHN were more abundant in visceral adipose tissue of ob/ob mice and diet-induced obese, insulin-resistant mice. High glucose and oxidative stress induced production of GS-HNE and GS-DHN by 3T3-L1 adipocytes in a GSTA4-dependent manner, and both glutathionylated metabolites induced secretion of tumor necrosis factor-α from RAW 264.7 and primary peritoneal macrophages. Targeted microarray analysis revealed GS-HNE and GS-DHN induced expression of inflammatory genes, including C3, C4b, c-Fos, igtb2, Nfkb1, and Nos2. Transgenic overexpression of GSTA4 in mouse adipose tissue led to increased production of GS-HNE associated with higher fasting glucose levels and moderately impaired glucose tolerance. These results indicated adipocyte oxidative stress results in GSTA4-dependent production of proinflammatory glutathione metabolites, GS-HNE and GS-DHN, which may represent a novel mechanism by which adipocyte dysfunction results in tissue inflammation and insulin resistance.

Relation between serum free fatty acids and adiposity, insulin resistance, and cardiovascular risk factors from adolescence to adulthood

The objective of this study was to describe longitudinal relations of serum total free fatty acids (FFAs) to insulin resistance (IR) and cardiovascular (CV) risk factors from adolescence into adulthood. The cohort included participants in a longitudinal study of obesity and IR with complete data, including anthropometric measures, FFAs, IR measured by euglycemic clamp, blood pressure, fasting serum lipids, and insulin at mean 15 and 22 years of age (n = 207) and their parents (n = 272). FFAs and IR were not significantly related at mean 15 years of age but were significantly related at mean age 22 years. FFA did not relate to BMI at either age. FFA at 15 years of age estimated IR at 22 years of age. In parents (mean age 51 years), FFA was significantly correlated with BMI, percent body fat, systolic blood pressure, LDL, and IR. Associations with all risk factors except IR in parents were attenuated by adjustment for BMI. Most 22 years of age correlations with parents were higher than corresponding 15 years of age correlations. This study finds that FFA is associated with IR starting in young adulthood. The relation between FFA and CV risk factors does not become significant until later adulthood. The results support a significant impact of early metabolic dysfunction on later CV risk.

Protein carbonylation, mitochondrial dysfunction, and insulin resistance

Oxidative stress has been identified as a common mechanism for cellular damage and dysfunction in a wide variety of disease states. Current understanding of the metabolic changes associated with obesity and the development of insulin resistance has focused on the role of oxidative stress and its interaction with inflammatory processes at both the tissue and organismal level. Obesity-related oxidative stress is an important contributing factor in the development of insulin resistance in the adipocyte as well as the myocyte. Moreover, oxidative stress has been linked to mitochondrial dysfunction, and this is thought to play a role in the metabolic defects associated with oxidative stress. Of the various effects of oxidative stress, protein carbonylation has been identified as a potential mechanism underlying mitochondrial dysfunction. As such, this review focuses on the relationship between protein carbonylation and mitochondrial biology and addresses those features that point to either the causal or casual relationship of lipid peroxidation-induced protein carbonylation as a determining factor in mitochondrial dysfunction.

Increased adipose protein carbonylation in human obesity

Insulin resistance is associated with obesity but mechanisms controlling this relationship in humans are not fully understood. Studies in animal models suggest a linkage between adipose reactive oxygen species (ROS) and insulin resistance. ROS oxidize cellular lipids to produce a variety of lipid hydroperoxides that in turn generate reactive lipid aldehydes that covalently modify cellular proteins in a process termed carbonylation. Mammalian cells defend against reactive lipid aldehydes and protein carbonylation by glutathionylation using glutathione-S-transferase A4 (GSTA4) or carbonyl reduction/oxidation via reductases and/or dehydrogenases. Insulin resistance in mice is linked to ROS production and increased level of protein carbonylation, mitochondrial dysfunction, decreased insulin-stimulated glucose transport, and altered adipokine secretion. To assess protein carbonylation and insulin resistance in humans, eight healthy participants underwent subcutaneous fat biopsy from the periumbilical region for protein analysis and frequently sampled intravenous glucose tolerance testing to measure insulin sensitivity. Soluble proteins from adipose tissue were analyzed using two-dimensional gel electrophoresis and the major carbonylated proteins identified as the adipocyte and epithelial fatty acid-binding proteins. The level of protein carbonylation was directly correlated with adiposity and serum free fatty acids (FFAs). These results suggest that in human obesity oxidative stress is linked to protein carbonylation and such events may contribute to the development of insulin resistance.