DR15-DQ6 remains dominantly protective against type 1 diabetes throughout the first five decades of life.

AIMS/HYPOTHESIS: Among white European children developing type 1 diabetes, the otherwise common HLA haplotype DR15-DQ6 is rare, and highly protective. Adult-onset type 1 diabetes is now known to represent more overall cases than childhood onset, but it is not known whether DR15-DQ6 is protective in older-adult-onset type 1 diabetes. We sought to quantify DR15-DQ6 protection against type 1 diabetes as age of onset increased. METHODS: In two independent cohorts we assessed the proportion of type 1 diabetes cases presenting through the first 50 years of life with DR15-DQ6, compared with population controls. In the After Diabetes Diagnosis Research Support System-2 (ADDRESS-2) cohort (n = 1458) clinician-diagnosed type 1 diabetes was confirmed by positivity for one or more islet-specific autoantibodies. In UK Biobank (n = 2502), we estimated type 1 diabetes incidence rates relative to baseline HLA risk for each HLA group using Poisson regression. Analyses were restricted to white Europeans and were performed in three groups according to age at type 1 diabetes onset: 0-18 years, 19-30 years and 31-50 years. RESULTS: DR15-DQ6 was protective against type 1 diabetes through to age 50 years (OR < 1 for each age group, all p < 0.001). The following ORs for type 1 diabetes, relative to a neutral HLA genotype, were observed in ADDRESS-2: age 5-18 years OR 0.16 (95% CI 0.08, 0.31); age 19-30 years OR 0.10 (0.04, 0.23); and age 31-50 years OR 0.37 (0.21, 0.68). DR15-DQ6 also remained highly protective at all ages in UK Biobank. Without DR15-DQ6, the presence of major type 1 diabetes high-risk haplotype (either DR3-DQ2 or DR4-DQ8) was associated with increased risk of type 1 diabetes. CONCLUSIONS/INTERPRETATION: HLA DR15-DQ6 confers dominant protection from type 1 diabetes across the first five decades of life.

Characterization of proinsulin-specific regulatory T cells in type 1 diabetes at different ages of onset.

BACKGROUND AND OBJECTIVES: Regulatory T cells (Tregs) play an important role in maintaining tolerance to self-antigens. Defects in the frequency and function of polyclonal Tregs have been reported in type 1 diabetes (T1D). However, characteristics of proinsulin (PI)-specific Tregs in human T1D have not yet been explored. Therefore, we aimed to characterize PI-specific Tregs in two distinct pathophysiological subtypes of T1D, juvenile-onset T1D (JOT1D) and adult-onset T1D (AOT1D), distinguished by the age of onset. METHODS: Peripheral blood mononuclear cells of the recruited subjects were stimulated in vitro with PI-derived peptides. PI-specific Tregs were characterized by flow cytometry using the combination of markers CD25, CD137, FOXP3 and CD45RA. RESULTS: Firstly, we observed similar frequencies of polyclonal Tregs in the T1D (n = 25) and healthy control (HC) (n = 20) subjects (P = 0.96), with a positive correlation between age and frequency of polyclonal Tregs (r = +0.35, P = 0.04). While the frequency of polyclonal Tregs was higher in AOT1D group (P = 0.02), both JOT1D (n = 14) and AOT1D groups (n = 11) had a comparable frequency of PI-specific Tregs in their peripheral blood. The frequency of PI-specific memory Tregs was significantly high in both the JOT1D (P = 0.02) and AOT1D (P = 0.009) groups compared to their respective HC groups (n = 10). Finally, we observed no significant difference in the expression of FOXP3 and IL-2 receptor in PI-specific Tregs in all the groups. CONCLUSIONS: Unlike polyclonal Tregs, both T1D subtypes harbor comparable frequencies of PI-specific Tregs. Chronic antigen presentation results in a distinct memory-like phenotype of PI-specific Tregs in these subjects irrespective of the age of disease onset.

Predicting misdiagnosed adult-onset type 1 diabetes using machine learning.

AIMS: It is now understood that almost half of newly diagnosed cases of type 1 diabetes are adult-onset. However, type 1 and type 2 diabetes are difficult to initially distinguish clinically in adults, potentially leading to ineffective care. In this study a machine learning model was developed to identify type 1 diabetes patients misdiagnosed as type 2 diabetes. METHODS: In this retrospective study, a machine learning model was developed to identify misdiagnosed type 1 diabetes patients from a population of patients with a prior type 2 diabetes diagnosis. Using Ambulatory Electronic Medical Records (AEMR), features capturing relevant information on age, demographics, risk factors, symptoms, treatments, procedures, vitals, or lab results were extracted from patients' medical history. RESULTS: The model identified age, BMI/weight, therapy history, and HbA1c/blood glucose values among top predictors of misdiagnosis. Model precision at low levels of recall (10 %) was 17 %, compared to <1 % incidence rate of misdiagnosis at the time of the first type 2 diabetes encounter in AEMR. CONCLUSIONS: This algorithm shows potential for being translated into screening guidelines or a clinical decision support tool embedded directly in an EMR system to reduce misdiagnosis of adult-onset type 1 diabetes and implement effective care at the outset.

Adult-onset type 1 diabetes: Predictors of glycaemic control.

AIMS: Knowledge about adult-onset (AO) type 1 diabetes remains insufficient. We sought to characterize the initial 5 years of AO type 1 diabetes and hypothesized that initial factors predictive of subsequent glycaemic control might exist. MATERIALS AND METHODS: A retrospective cohort study based on electronic medical records of 280 subjects with newly diagnosed AO type 1 diabetes (>18 years of age, excluding secondary and latent autoimmune diabetes) with available data for the initial 5-year treatment. RESULTS: Characteristics at diagnosis: 61% men, mean age 37 ± 12 years, BMI 23 ± 3.3 (kg/m2), systolic/diastolic blood pressure: 123 ± 15/76 ± 9 mm Hg and LDL cholesterol: 2.9 ± 0.9 mmol/L. HbA1c decreased from 106 mmol/mol (11.8%) at diagnosis to 52 mmol/mol (6.9%) at 6 months and then increased gradually to 67 mmol/mol (8.3%) after 5 years. Strict glycaemic control (<53 mmol/mol (7%)) was achieved by 66% within 6-9 months and 30% after 5 years. Comparing patients with and without strict glycaemic control after 5 years revealed no differences in HbA1c, C-peptide or any other diabetes-related parameter at the time of diagnosis. However, reaching strict control within 6-9 months after diagnosis was strongly associated with remaining in strict control after 5 years (OR: 9.2 (CI-95% 4.0-20.9; P < 0.0001)). Conversely, patients who did not achieve early strict control were very unlikely to be well controlled after 5 years. CONCLUSIONS: Long-term tight glycaemic control in subjects with AO type 1 diabetes is both achievable and to some extent predictable. Whether alternative strategies shortly after diagnosis would benefit patients with insufficient glycaemic control should be investigated.