Islet Autoantibody Level Distribution in Type 1 Diabetes and Their Association With Genetic and Clinical Characteristics

Islet autoantibodies in Thai individuals diagnosed with type 1 diabetes before 30 years of age: a large multicentre nationwide study

AIMS/HYPOTHESIS

Type 1 diabetes is categorised into autoantibody positive and autoantibody negative. Most type 1 diabetes research has focused on European populations, leaving a gap in understanding in relation to other ethnic groups, including Thai populations. This lack of data is significant given Thailand's poor prevention and therapeutic management strategies. We aimed to investigate the frequency and distribution of islet autoantibodies among Thai individuals with long-standing type 1 diabetes diagnosed before the age of 30 years.

METHODS

We conducted a nationwide population-based study involving 48 hospitals in Thailand from May 2020 to September 2023, enrolling 953 participants. Demographic and clinical characteristics of individuals with autoantibody-positive and -negative type 1 diabetes were analysed. The autoantibodies GAD65, IA-2 and ZnT8 were measured using ELISA. A random C-peptide level was detected by electrochemiluminescence immunoassay.

RESULTS

Thai individuals with autoantibody-negative type 1 diabetes comprised 34.2% of the population. Among all individuals, the frequency of GAD65, IA-2 and ZnT8 was 56%, 37% and 33%, respectively. Autoantibody-negative individuals with type 1 diabetes were older at diagnosis, had higher BMI and had higher random C-peptide levels compared with autoantibody-positive individuals with type 1 diabetes. Female individuals had a higher prevalence of type 1 diabetes than male individuals (58% vs 42%; p=1.531 × 10-5). The southern region of Thailand exhibited a distinct pattern of autoantibody frequency compared with other regions (p=0.0001561).

CONCLUSIONS/INTERPRETATION

The frequency, distribution and characteristics of autoantibody-positive and -negative long-standing type 1 diabetes in Thailand showed uniqueness from other populations. This provides insight into the disease that may have implications for type 1 diabetes prediction, treatment and pathogenesis, especially in the Southeast Asian population.

Type 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array-based approaches

A Type 1 Diabetes Genetic Risk Score (T1DGRS) aids diagnosis and prediction of Type 1 Diabetes (T1D). While traditionally derived from imputed array genotypes, Whole Genome Sequencing (WGS) provides a more direct approach and is now increasingly used in clinical and research studies. We investigated the concordance between WGS-based and array-based T1DGRS across genetic ancestries in 149,265 UK Biobank participants using WGS, TOPMed-imputed, and 1000 Genomes-imputed array genotypes. In the overall cohort, WGS-based T1DGRS demonstrated strong correlation with TOPMed-imputed array-based score (r = 0.996, average WGS-based score 0.0028 standard deviations (SD) lower, p < 10- 31), while showing lower correlation with 1000 Genomes-imputed array-based scores (r = 0.981, 0.043 SD lower in WGS, p < 10- 300). Ancestry-stratified analyses between WGS-based and TOPMed-imputed array-based score showed the highest correlation with European ancestry (r = 0.996, 0.044 SD lower in WGS, p < 10- 300) followed by African ancestry (r = 0.989, 0.0193 SD lower in WGS, p < 10- 14) and South Asian ancestry (r = 0.986, 0.0129 SD lower in WGS, p < 10 - 6). These differences were more pronounced when comparing WGS based score with 1000 Genomes-imputed array-based scores (r = 0.982, 0.975, 0.957 for European, South Asian, African respectively). Population-level analysis using WGS-based T1DGRS revealed significant ancestry-based stratification, with European ancestry individuals showing the highest scores, followed by South Asian (average 0.28 SD lower than Europeans, p < 10- 58) and African ancestry individuals (average 0.89 SD lower than Europeans, p < 10- 300). Notably, when applying the European ancestry-derived 90th centile risk threshold, only 0.71% (95% CI 0.41-1.13) of African ancestry individuals and 6.4% (95% CI 5.6-7.2) of South Asian individuals were identified as high-risk, substantially below the expected 10%. In conclusion, while WGS is viable for generating T1DGRS, with TOPMed-imputed genotypes offering a cost-effective alternative, the persistence of ancestry-based variations in T1DGRS distribution even using whole genome sequencing emphasises the need for ancestry-specific or pan-ancestry standards in clinical practice.

Type 1 diabetes, celiac disease, and autoimmune thyroiditis autoantibodies in population-based type 2 diabetes patients

Aims

The study aims were to determine autoantibodies associated with type 1 diabetes (T1D), celiac disease (CD) and autoimmune thyroid disease (AITD) in individuals living with type 2 diabetes (T2D) compared to T1D and matched controls.

Methods

Individuals with T1D and T2D were randomly identified in health-care registers. Blood was collected through home-capillary sampling and autoantibodies associated with either T1D against glutamic acid decarboxylase (GADA), insulin (IAA), insulinoma antigen-2 (IA-2A), and zinc transporter 8 (ZnT8A), CD against tissue transglutaminase (tTGA) or AITD against thyroid peroxidase (TPOA) were determined in an automated, multiplex Antibody Detection by Agglutination-PCR (ADAP) assay.

Results

GADA were detected in 46 % (88/191) of T1D and increased to 6.2 % (23/372) in T2D compared to 2.6 % (7/259) of controls (p = 0.0367). tTGA was low (1.1-2.6 %) and not different in between the study cohorts, nonetheless, in T1D tTGA was associated to islet autoantibodies. TPOA was more frequent in T1D, 27.1 % (53/191), compared to either T2D, 14.8 % (55/372; p = 0.0002) or controls, 14.3 % (37/259) (p = 0.0004). Overall, TPOA was more frequent in GADA positive (34.8 %; 8/23) than negative (13.5 %; 47/349; p = 0.0053) T2D individuals.

Conclusion

It's suggested that analyzing GADA and TPOA may refine the autoimmune landscape in individuals clinically classified as T2D.

Clinical features, biomarkers and diabetic ketoacidosis at diagnosis of type 1 diabetes among children and adolescents in Sana'a, Yemen

INTRODUCTION

There is little published information on type 1 diabetes (T1D) in children in Yemen. We aimed to identify the clinical characteristics, biomarkers and diabetic ketoacidosis (DKA) at diagnosis of T1D among children and adolescents in a diabetes centre in Sana'a, Yemen.

METHODS

A total of 485 children and adolescents aged ≤18 years diagnosed with T1D during the period 2010-2020 were included in the study. The variables investigated were demographic and clinical characteristics, biomarkers, subtypes of T1D, and the risk factors for severe DKA at diagnosis.

RESULTS

At diagnosis, children aged <10 years compared with those aged ≥10 years had higher mean plasma glucose (p<0.001) and mean HbA1c (p=0.026), and lower mean C-peptide (pmol/L) (p=0.019), and a higher frequency of DKA at diagnosis than older children (p<0.001). A majority of the study population (383, 79%) presented in DKA . Children aged <10 years presenting with DKA had significantly longer median appraisal interval (p=0.009) and median total diagnosis interval (p=0.025), and significantly lower mean C-peptide (p=0.001) as compared with their peers without DKA. The prevalence of autoantibody-negative 'idiopathic' T1D was 36 (32%) of the total number tested for autoantibody and familial T1D 61 (12.6%) of all the study population.

CONCLUSION

In Yemen children aged <10 years with new-onset T1D frequently faced the challenge of a delay in diagnosis and treatment initiation, with severe hyperglycaemia and a higher risk of DKA at diagnosis.

The challenges of identifying and studying type 1 diabetes in adults

Diagnosing type 1 diabetes in adults is difficult since type 2 diabetes is the predominant diabetes type, particularly with an older age of onset (approximately >30 years). Misclassification of type 1 diabetes in adults is therefore common and will impact both individual patient management and the reported features of clinically classified cohorts. In this article, we discuss the challenges associated with correctly identifying adult-onset type 1 diabetes and the implications of these challenges for clinical practice and research. We discuss how many of the reported differences in the characteristics of autoimmune/type 1 diabetes with increasing age of diagnosis are likely explained by the inadvertent study of mixed populations with and without autoimmune aetiology diabetes. We show that when type 1 diabetes is defined by high-specificity methods, clinical presentation, islet-autoantibody positivity, genetic predisposition and progression of C-peptide loss remain broadly similar and severe at all ages and are unaffected by onset age within adults. Recent clinical guidance recommends routine islet-autoantibody testing when type 1 diabetes is clinically suspected or in the context of rapid progression to insulin therapy after a diagnosis of type 2 diabetes. In this moderate or high prior-probability setting, a positive islet-autoantibody test will usually confirm autoimmune aetiology (type 1 diabetes). We argue that islet-autoantibody testing of those with apparent type 2 diabetes should not be routinely undertaken as, in this low prior-prevalence setting, the positive predictive value of a single-positive islet antibody for autoimmune aetiology diabetes will be modest. When studying diabetes, extremely high-specificity approaches are needed to identify autoimmune diabetes in adults, with the optimal approach depending on the research question. We believe that until these recommendations are widely adopted by researchers, the true phenotype of late-onset type 1 diabetes will remain largely misunderstood.

Autoimmune Diabetes From Childhood to Adulthood: The Role of Pancreatic Autoantibodies and HLA-DRB1 Genotype

CONTEXT

Autoimmune diabetes can develop at any age, but unlike early-onset diabetes, adult onset is less well documented. We aimed to compare, over a wide age range, the most reliable predictive biomarkers for this pathology: pancreatic-autoantibodies and HLA-DRB1 genotype.

METHODS

A retrospective study of 802 patients with diabetes (aged 11 months to 66 years) was conducted. Pancreatic autoantibodies at diagnosis: insulin autoantibodies (IAA), glutamate decarboxylase autoantibodies (GADA), islet tyrosine phosphatase 2 autoantibodies (IA2A), and zinc transporter-8 autoantibodies (ZnT8A) and HLA-DRB1 genotype were analyzed.

RESULTS

Compared with early-onset patients, adults had a lower frequency of multiple autoantibodies, with GADA being the most common. At early onset, IAA was the most frequent in those younger than 6 years and correlated inversely with age; GADA and ZnT8A correlated directly and IA2A remained stable.The absence of HLA-DRB1 risk genotype was associated with higher age at diabetes onset (27.5 years; interquartile range [IQR], 14.3-35.7), whereas the high-risk HLA-DR3/DR4 was significantly more common at lower age (11.9 years; IQR, 7.1-21.6). ZnT8A was associated with DR4/non-DR3 (odds ratio [OR], 1.91; 95% CI, 1.15-3.17), GADA with DR3/non-DR4 (OR, 2.97; 95% CI, 1.55-5.71), and IA2A with DR4/non-DR3 and DR3/DR4 (OR, 3.89; 95% CI, 2.28-6.64, and OR, 3.08; 95% CI, 1.83-5.18, respectively). No association of IAA with HLA-DRB1 was found.

CONCLUSION

Autoimmunity and HLA-DRB1 genotype are age-dependent biomarkers. Adult-onset autoimmune diabetes is associated with lower genetic risk and lower immune response to pancreatic islet cells compared with early-onset diabetes.

The importance of biomarker development for monitoring type 1 diabetes progression rate and therapeutic responsiveness

Type 1 diabetes (T1D) is an autoimmune condition of children and adults in which immune cells target insulin-producing pancreatic β-cells for destruction. This results in a chronic inability to regulate blood glucose levels. The natural history of T1D is well-characterized in childhood. Evidence of two or more autoantibodies to the islet antigens insulin, GAD, IA-2 or ZnT8 in early childhood is associated with high risk of developing T1D in the future. Prediction of risk is less clear in adults and, overall, the factors controlling the progression rate from multiple islet autoantibody positivity to onset of symptoms are not fully understood. An anti-CD3 antibody, teplizumab, was recently shown to delay clinical progression to T1D in high-risk individuals including adults and older children. This represents an important proof of concept for those at risk of future T1D. Given their role in risk assessment, islet autoantibodies might appear to be the most obvious biomarkers to monitor efficacy. However, monitoring islet autoantibodies in clinical trials has shown only limited effects, although antibodies to the most recently identified autoantigen, tetraspanin-7, have not yet been studied in this context. Measurements of beta cell function remain fundamental to assessing efficacy and different models have been proposed, but improved biomarkers are required for both progression studies before onset of diabetes and in therapeutic monitoring. In this mini-review, we consider some established and emerging predictive and prognostic biomarkers, including markers of pancreatic function that could be integrated with metabolic markers to generate improved strategies to measure outcomes of therapeutic intervention.