Haptoglobin genotype predicts development of coronary artery calcification in a prospective cohort of patients with type 1 diabetes

<p>Abstract</p> <p>Background</p> <p>Coronary artery disease has been linked with genotypes for haptoglobin (Hp) which modulates extracorpuscular hemoglobin. We hypothesized that the Hp genotype would predict progression of coronary artery calcification (CAC), a marker of subclinical atherosclerosis.</p> <p>Methods</p> <p>CAC was measured three times in six years among 436 subjects with type 1 diabetes and 526 control subjects participating in the Coronary Artery Calcification in Type 1 Diabetes (CACTI) study. Hp typing was performed on plasma samples by polyacrylamide gel electrophoresis.</p> <p>Results</p> <p>The Hp 2-2 genotype predicted development of significant CAC only in subjects with diabetes who were free of CAC at baseline (OR: 1.95, 95% CI: 1.07-3.56, p = 0.03), compared to those without the Hp 2-2 genotype, controlling for age, sex, blood pressure and HDL-cholesterol. Hp 2 appeared to have an allele-dose effect on development of CAC. Hp genotype did not predict CAC progression in individuals without diabetes.</p> <p>Conclusions</p> <p>Hp genotype may aid prediction of accelerated coronary atherosclerosis in subjects with type 1 diabetes.</p

Components of Metabolic Syndrome and 5-Year Chance in Insulin Clearance - The Resistance Atherosclerosis Study (IRAS)

Aims Cross-sectional evidence indicates that abdominal adiposity, hypertension, dyslipidaemia and glycaemia are associated with reduced metabolic clearance rate of insulin (MCRI). Little is known about the progression of MCRI and whether components of metabolic syndrome are associated with the change in MCRI. In this study, we examined the association between components of metabolic syndrome and the 5-year change of MCRI. Methods At baseline and 5-year follow-up, we measured fasting plasma triglycerides (TG), high-density lipoprotein (HDL) cholesterol, blood pressure (BP), waist circumference (WC) and fasting blood glucose (FBG) in 784 non-diabetic participants in the Insulin Resistance Atherosclerosis Study. MCRI, insulin sensitivity (SI) and acute insulin response (AIR) were determined from frequently sampled intravenous glucose tolerance tests. Results We observed a 29% decline of MCRI at follow-up. TG, systolic BP and WC at baseline were inversely associated with a decline of MCRI regression models adjusted for age, sex, ethnicity, smoking, alcohol consumption, energy expenditure, family history of diabetes, BMI, SI and AIR [β = −0.057 (95% confidence interval, CI: −0.11, −0.0084) for TG, β = −0.0019 (95% CI: −0.0035, −0.00023) for systolic BP and β  = −0.0084 (95% CI: −0.013, −0.0039) for WC; all p \u3c 0.05]. Higher HDL cholesterol at baseline was associated with an increase in MCRI [multivariable-adjusted β = 0.0029 (95% CI: 0.0010, 0.0048), p = 0.002]. FBG at baseline was not associated with MCRI at follow-up [multivariable-adjusted β = 0.0014 (95% CI: −0.0026, 0.0029)]. Conclusions MCRI declined progressively over 5 years in a non-diabetic cohort. Components of metabolic syndrome at baseline were associated with a significant change in MCRI

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

Type 1 diabetes (T1D) is a disease of insulin deficiency that results from autoimmune destruction of pancreatic islet β cells. The exact cause of T1D remains unknown, although asymptomatic islet autoimmunity lasting from weeks to years before diagnosis raises the possibility of intervention before the onset of clinical disease. The number, type, and titer of islet autoantibodies are associated with long-term disease risk but do not cause disease, and robust early predictors of individual progression to T1D onset remain elusive. The Environmental Determinants of Diabetes in the Young (TEDDY) consortium is a prospective cohort study aiming to determine genetic and environmental interactions causing T1D. Here, we analyzed longitudinal blood transcriptomes of 2013 samples from 400 individuals in the TEDDY study before both T1D and islet autoimmunity. We identified and interpreted age-associated gene expression changes in healthy infancy and age-independent changes tracking with progression to both T1D and islet autoimmunity, beginning before other evidence of islet autoimmunity was present. We combined multivariate longitudinal data in a Bayesian joint model to predict individual risk of T1D onset and validated the association of a natural killer cell signature with progression and the model's predictive performance on an additional 356 samples from 56 individuals in the independent Type 1 Diabetes Prediction and Prevention study. Together, our results indicate that T1D is characterized by early and longitudinal changes in gene expression, informing the immunopathology of disease progression and facilitating prediction of its course.The TEDDY Study is funded by U01 DK63829, U01 DK63861, U01 DK63821, U01 DK63865, U01 DK63863, U01 DK63836, U01 DK63790, UC4 DK63829, UC4 DK63861, UC4 DK63821, UC4 DK63865, UC4 DK63863, UC4 DK63836, UC4 DK95300, UC4 DK100238, UC4 DK106955, UC4 DK112243, UC4 DK117483, and Contract No. HHSN267200700014C from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institute of Environmental Health Sciences (NIEHS), Centers for Disease Control and Prevention (CDC), and JDRF. This work supported in part by the NIH/NCATS Clinical and Translational Science Awards to the University of Florida (UL1 TR000064) and the University of Colorado (UL1 TR001082). KGCS is a Lister Prize fellow and is supported by a Wellcome Trust Senior Investigator award (200871/Z/16/Z). EFM is a Wellcome-Beit prize fellow (10406/Z/14/A) supported by the Wellcome Trust and Beit Foundation (10406/Z/14/Z) and by the National Institutes for Health Research Biomedical Research Centre (Cambridge). LPX’s affiliation changed after completion of the manuscript and is now Département d'informatique et de recherche opérationnelle, Université de Montréal, Montréal, Canada and Mila, Quebec Institute for Learning Algorithms, Montréal, Canada

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

Screening for Type 1 Diabetes in the General Population:A Status Report and Perspective

Most screening programs to identify individuals at risk for type 1 diabetes have targeted relatives of people living with the disease to improve yield and feasibility. However, ∼90% of those who develop type 1 diabetes do not have a family history. Recent successes in disease-modifying therapies to impact the course of early-stage disease have ignited the consideration of the need for and feasibility of population screening to identify those at increased risk. Existing population screening programs rely on genetic or autoantibody screening, and these have yielded significant information about disease progression and approaches for timing for screening in clinical practice. At the March 2021 Type 1 Diabetes TrialNet Steering Committee meeting, a session was held in which ongoing efforts for screening in the general population were discussed. This report reviews the background of these efforts and the details of those programs. Additionally, we present hurdles that need to be addressed for successful implementation of population screening and provide initial recommendations for individuals with positive screens so that standardized guidelines for monitoring and follow-up can be established

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

Characteristics of slow progression to diabetes in multiple islet autoantibody-positive individuals from five longitudinal cohorts:the SNAIL study

Aims/hypothesis Multiple islet autoimmunity increases risk of diabetes, but not all individuals positive for two or more islet autoantibodies progress to disease within a decade. Major islet autoantibodies recognise insulin (IAA), GAD (GADA), islet antigen-2 (IA-2A) and zinc transporter 8 (ZnT8A). Here we describe the baseline characteristics of a unique cohort of ‘slow progressors’ (n = 132) who were positive for multiple islet autoantibodies (IAA, GADA, IA-2A or ZnT8A) but did not progress to diabetes within 10 years. Methods Individuals were identified from five studies (BABYDIAB, Germany; Diabetes Autoimmunity Study in the Young [DAISY], USA; All Babies in Southeast Sweden [ABIS], Sweden; Bart’s Oxford Family Study [BOX], UK and the Pittsburgh Family Study, USA). Multiple islet autoantibody characteristics were determined using harmonised assays where possible. HLA class II risk was compared between slow progressors and rapid progressors (n = 348 diagnosed <5 years old from BOX) using the χ2 test. Results In the first available samples with detectable multiple antibodies, the most frequent autoantibodies were GADA (92%), followed by ZnT8A (62%), IAA (59%) and IA-2A (41%). High risk HLA class II genotypes were less frequent in slow (28%) than rapid progressors (42%, p = 0.011), but only two slow progressors carried the protective HLA DQ6 allele. Conclusion No distinguishing characteristics of slow progressors at first detection of multiple antibodies have yet been identified. Continued investigation of these individuals may provide insights into slow progression that will inform future efforts to slow or prevent progression to clinical diabetes

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